{"id":3389,"date":"2026-09-08T19:45:55","date_gmt":"2026-09-08T12:45:55","guid":{"rendered":"https:\/\/dgway.com\/blog_E\/?p=3389"},"modified":"2026-09-08T20:40:10","modified_gmt":"2026-09-08T13:40:10","slug":"remote-sensing-image-enhancement-for-next-generation-satellite-services","status":"publish","type":"post","link":"https:\/\/dgway.com\/blog_E\/2026\/09\/08\/remote-sensing-image-enhancement-for-next-generation-satellite-services\/","title":{"rendered":"Remote Sensing Image Enhancement for Next-Generation Satellite Services"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"211\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-1024x211.png\" alt=\"\" class=\"wp-image-3390\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-1024x211.png 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-300x62.png 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-767x158.png 767w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-1536x316.png 1536w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Alinx-AX7010-2048x422.png 2048w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Improving satellite image quality can follow two paths. The conventional path depends on new hardware investment, while the <strong>HybridATNet<\/strong> path enhances the value of images already captured by existing sensors. Developed from research at Chulalongkorn University, HybridATNet applies AI-based super-resolution to improve the clarity and usefulness of remote sensing imagery.<\/p>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd0d Overview<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nFor satellite service providers and remote sensing users, improving spatial image quality does not always require replacing the payload or sensor system. AI-based super-resolution can enhance existing image archives and incoming imagery within the current image-processing workflow.\n<\/p>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Comparison Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-compare-grid {\n    display: grid;\n    grid-template-columns: 1fr 1fr;\n    gap: 24px;\n    margin-bottom: 48px;\n  }\n  .dg-compare-box {\n    background-color: #f8fafc;\n    border: 1px solid #e2e8f0;\n    border-top: 4px solid #cbd5e1;\n    border-radius: 8px;\n    padding: 24px;\n  }\n  .dg-compare-box.highlight {\n    background-color: #f0f9ff;\n    border-color: #bae6fd;\n    border-top: 4px solid #0284c7;\n  }\n  .dg-compare-box h3 {\n    margin-top: 0;\n    margin-bottom: 16px;\n    font-size: 1.8rem;\n    color: #0f172a;\n    font-weight: 600;\n  }\n  .dg-compare-box.highlight h3 {\n    color: #0369a1;\n  }\n  \n  \/* Custom List Styles *\/\n  .dg-compare-box ul {\n    list-style: none;\n    margin: 0;\n    padding: 0;\n  }\n  .dg-compare-box li {\n    position: relative;\n    padding: 12px 0 12px 32px;\n    border-bottom: 1px solid #e2e8f0; \/* \u0e40\u0e2a\u0e49\u0e19\u0e41\u0e1a\u0e48\u0e07\u0e02\u0e49\u0e2d\u0e04\u0e27\u0e32\u0e21 *\/\n    color: #475569;\n  }\n  .dg-compare-box li:last-child {\n    border-bottom: none; \/* \u0e40\u0e2d\u0e32\u0e40\u0e2a\u0e49\u0e19\u0e41\u0e1a\u0e48\u0e07\u0e2d\u0e31\u0e19\u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22\u0e2d\u0e2d\u0e01 *\/\n    padding-bottom: 0;\n  }\n  .dg-compare-box li:first-child {\n    padding-top: 4px;\n  }\n  \n  \/* Icon styles *\/\n  .dg-compare-box li::before {\n    content: \"\";\n    position: absolute;\n    left: 0;\n    top: 14px;\n    width: 20px;\n    height: 20px;\n    background-size: contain;\n    background-repeat: no-repeat;\n  }\n  \/* Icon for Conventional Path (Gray Minus) *\/\n  .dg-compare-box:not(.highlight) li::before {\n    background-image: url(\"data:image\/svg+xml,%3Csvg xmlns='http:\/\/www.w3.org\/2000\/svg' fill='none' viewBox='0 0 24 24' stroke='%2394a3b8'%3E%3Cpath stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M5 12h14'%3E%3C\/path%3E%3C\/svg%3E\");\n    top: 15px;\n  }\n  \/* Icon for HybridATNet Path (Blue Plus) *\/\n  .dg-compare-box.highlight li::before {\n    background-image: url(\"data:image\/svg+xml,%3Csvg xmlns='http:\/\/www.w3.org\/2000\/svg' fill='none' viewBox='0 0 24 24' stroke='%230284c7'%3E%3Cpath stroke-linecap='round' stroke-linejoin='round' stroke-width='3' d='M12 5v14M5 12h14'%3E%3C\/path%3E%3C\/svg%3E\");\n    top: 14px;\n  }\n\n  @media (max-width: 768px) {\n    .dg-compare-grid {\n      grid-template-columns: 1fr;\n    }\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Comparison Section (\u0e15\u0e32\u0e23\u0e32\u0e07 2 \u0e0a\u0e48\u0e2d\u0e07) -->\n  <div class=\"dg-compare-grid\">\n    <div class=\"dg-compare-box\">\n      <h3>Conventional Path: New Hardware<\/h3>\n      <ul>\n        <li>Upgrade sensors to improve spatial resolution.<\/li>\n        <li>Requires a long development and deployment timeline.<\/li>\n        <li>Requires significant investment before the service upgrade.<\/li>\n        <li>Existing image archives remain unchanged.<\/li>\n      <\/ul>\n    <\/div>\n    <div class=\"dg-compare-box highlight\">\n      <h3>HybridATNet Path: AI Enhancement<\/h3>\n      <ul>\n        <li>Enhance images from existing sensors.<\/li>\n        <li>Integrate into the current image-processing flow.<\/li>\n        <li>Increase the value of both archived and live images.<\/li>\n        <li>Improve service quality without requiring a new payload.<\/li>\n      <\/ul>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\u2699\ufe0f What Makes HybridATNet Different?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nHybridATNet combines multiple image-understanding techniques into a single processing pipeline, helping preserve both fine details and large-scale structures commonly found in satellite and aerial imagery.\n<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/HyBridANet-1024x576.png\" alt=\"\" class=\"wp-image-3410\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/HyBridANet-1024x576.png 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/HyBridANet-768x432.png 768w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/HyBridANet-300x169.png 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/HyBridANet.png 1512w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">HybridATNet network architecture overview.<\/figcaption><\/figure>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Features Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-features-grid {\n    display: grid;\n    grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));\n    gap: 20px;\n    margin-bottom: 40px;\n  }\n  .dg-feature-card {\n    background-color: #ffffff;\n    border: 1px solid #e0f2fe;\n    border-radius: 8px;\n    padding: 24px;\n    box-shadow: 0 2px 4px rgba(2, 132, 199, 0.04);\n    transition: transform 0.2s ease, box-shadow 0.2s ease;\n  }\n  .dg-feature-card:hover {\n    transform: translateY(-2px);\n    box-shadow: 0 4px 12px rgba(2, 132, 199, 0.08);\n  }\n  .dg-feature-card h4 {\n    margin-top: 0;\n    margin-bottom: 12px;\n    font-size: 1.8rem;\n    color: #0284c7;\n    font-weight: 600;\n  }\n  .dg-feature-card p {\n    margin: 0;\n    font-size: 1.5rem;\n    color: #475569;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Features Section (\u0e01\u0e25\u0e48\u0e2d\u0e07\u0e41\u0e22\u0e01\u0e2b\u0e31\u0e27\u0e02\u0e49\u0e2d) -->\n  <div class=\"dg-features-grid\">\n    <div class=\"dg-feature-card\">\n      <h4>Enhanced Channel Attention<\/h4>\n      <p>Identifies important image features and helps the model focus on meaningful information instead of treating all feature channels equally.<\/p>\n    <\/div>\n    \n    <div class=\"dg-feature-card\">\n      <h4>Multi-Scale Spatial Attention<\/h4>\n      <p>Analyzes image structures at multiple scales, helping the model recognize both small objects and larger geographical patterns.<\/p>\n    <\/div>\n    \n    <div class=\"dg-feature-card\">\n      <h4>Residual Atrous Spatial Pyramid Pooling<\/h4>\n      <p>Expands the effective field of view without excessive computational cost, enabling broader contextual understanding.<\/p>\n    <\/div>\n    \n    <div class=\"dg-feature-card\">\n      <h4>Hybrid CNN-Transformer Architecture<\/h4>\n      <p>Combines local-detail extraction from CNNs with global scene understanding from Transformer networks.<\/p>\n    <\/div>\n    \n    <div class=\"dg-feature-card\">\n      <h4>Sparse Transformer Processing<\/h4>\n      <p>Captures long-range relationships across the image while reducing computational complexity compared with conventional Transformer approaches.<\/p>\n    <\/div>\n    \n    <div class=\"dg-feature-card\">\n      <h4>Hybrid Feature Refinement<\/h4>\n      <p>Processes image information in both spatial and frequency domains to sharpen structures, preserve edges, and suppress unwanted noise.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\ude80 Performance Validation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nHybridATNet was validated using the AID remote sensing benchmark dataset at the challenging <strong>\u00d74 enhancement scale<\/strong>. In the source material, it outperformed traditional interpolation and several well-known super-resolution models in both PSNR and SSIM.\n<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison-1024x768.png\" alt=\"\" class=\"wp-image-3411\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison-1024x768.png 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison-300x225.png 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison-768x576.png 768w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison-1536x1152.png 1536w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Comparison.png 1953w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Comparison of HybridATNet with other super-resolution methods.<\/figcaption><\/figure>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Table Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-table-wrap {\n    overflow-x: auto;\n    border-radius: 8px;\n    border: 1px solid #e2e8f0;\n    background: #ffffff;\n    box-shadow: 0 2px 4px rgba(2, 132, 199, 0.04);\n    margin-bottom: 40px;\n  }\n\n  .dg-table {\n    width: 100%;\n    border-collapse: collapse;\n    min-width: 600px;\n  }\n\n  .dg-table th, .dg-table td {\n    padding: 16px 20px;\n    border-bottom: 1px solid #e2e8f0;\n    text-align: left;\n    color: #475569;\n  }\n\n  .dg-table th {\n    background-color: #f0f9ff;\n    color: #0369a1;\n    font-weight: 600;\n  }\n\n  .dg-table tr:last-child td {\n    border-bottom: none;\n  }\n\n  \/* Highlight Row Style *\/\n  .dg-table tr.highlight-row {\n    background-color: #e0f2fe; \/* Light blue background *\/\n  }\n  .dg-table tr.highlight-row td {\n    color: #0c4a6e; \/* Darker blue text for contrast *\/\n    font-weight: 700;\n  }\n<\/style>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Table Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-table-wrap {\n    overflow-x: auto;\n    border-radius: 8px;\n    border: 1px solid #e2e8f0;\n    background: #ffffff;\n    box-shadow: 0 2px 4px rgba(2, 132, 199, 0.04);\n    margin-bottom: 12px; \/* \u0e25\u0e14 margin \u0e25\u0e07\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e43\u0e2b\u0e49 caption \u0e2d\u0e22\u0e39\u0e48\u0e43\u0e01\u0e25\u0e49\u0e15\u0e32\u0e23\u0e32\u0e07 *\/\n  }\n\n  .dg-table {\n    width: 100%;\n    border-collapse: collapse;\n    min-width: 600px;\n  }\n\n  .dg-table th, .dg-table td {\n    padding: 16px 20px;\n    border-bottom: 1px solid #e2e8f0;\n    text-align: left;\n    color: #475569;\n  }\n\n  .dg-table th {\n    background-color: #f0f9ff;\n    color: #0369a1;\n    font-weight: 600;\n  }\n\n  .dg-table tr:last-child td {\n    border-bottom: none;\n  }\n\n  \/* Highlight Row Style *\/\n  .dg-table tr.highlight-row {\n    background-color: #e0f2fe;\n  }\n  .dg-table tr.highlight-row td {\n    color: #0c4a6e;\n    font-weight: 700;\n  }\n  \n  \/* Table Caption \/ Note Style *\/\n  .dg-table-caption {\n    font-size: 0.9rem;\n    color: #64748b; \/* \u0e2a\u0e35\u0e40\u0e17\u0e32\u0e2d\u0e21\u0e1f\u0e49\u0e32\u0e43\u0e2b\u0e49\u0e14\u0e39\u0e40\u0e1b\u0e47\u0e19\u0e04\u0e33\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22 *\/\n    margin-top: 0;\n    margin-bottom: 40px;\n    line-height: 1.5;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Table Section -->\n  <div class=\"dg-table-wrap\">\n    <table class=\"dg-table\">\n      <thead>\n        <tr>\n          <th>Method<\/th>\n          <th>Approach<\/th>\n          <th>PSNR (dB)<\/th>\n          <th>SSIM<\/th>\n        <\/tr>\n      <\/thead>\n      <tbody>\n        <tr>\n          <td>Bicubic<\/td>\n          <td>Traditional interpolation<\/td>\n          <td>27.30<\/td>\n          <td>0.7036<\/td>\n        <\/tr>\n        <tr>\n          <td>SRCNN<\/td>\n          <td>Early CNN-based SR<\/td>\n          <td>28.48<\/td>\n          <td>0.7565<\/td>\n        <\/tr>\n        <tr>\n          <td>VDSR<\/td>\n          <td>Deep CNN-based SR<\/td>\n          <td>28.99<\/td>\n          <td>0.7753<\/td>\n        <\/tr>\n        <tr>\n          <td>SRFormer<\/td>\n          <td>Transformer-based SR<\/td>\n          <td>29.40<\/td>\n          <td>0.7911<\/td>\n        <\/tr>\n        <!-- Highlighted Row -->\n        <tr class=\"highlight-row\">\n          <td>HybridATNet<\/td>\n          <td>Hybrid CNN-Transformer SR<\/td>\n          <td>29.47<\/td>\n          <td>0.7924<\/td>\n        <\/tr>\n      <\/tbody>\n    <\/table>\n  <\/div>\n  <!-- \u0e2a\u0e48\u0e27\u0e19\u0e19\u0e35\u0e49\u0e04\u0e37\u0e2d Caption \u0e14\u0e49\u0e32\u0e19\u0e25\u0e48\u0e32\u0e07\u0e15\u0e32\u0e23\u0e32\u0e07 -->\n  <p class=\"dg-table-caption\">\n    * Higher PSNR and SSIM indicate better reconstruction quality. HybridATNet achieved the highest score among the selected comparison methods while maintaining an efficient architecture.\n  <\/p>\n<\/div>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Metrics Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-metrics-container {\n    display: flex;\n    flex-wrap: wrap;\n    justify-content: center;\n    gap: 24px;\n    margin-bottom: 48px;\n  }\n\n  .dg-metric-box {\n    background-color: #ffffff;\n    border: 1px solid #e0f2fe;\n    border-radius: 8px;\n    padding: 32px 24px;\n    text-align: center;\n    box-shadow: 0 2px 4px rgba(2, 132, 199, 0.04);\n    flex: 1 1 300px;\n    max-width: 360px;\n    transition: transform 0.2s ease, box-shadow 0.2s ease;\n  }\n\n  .dg-metric-box:hover {\n    transform: translateY(-2px);\n    box-shadow: 0 4px 12px rgba(2, 132, 199, 0.08);\n  }\n\n  .dg-metric-value {\n    display: block;\n    font-size: 3rem;\n    font-weight: 800;\n    color: #0284c7;\n    line-height: 1;\n    margin-bottom: 12px;\n  }\n\n  .dg-metric-label {\n    display: block;\n    font-size: 1.4rem;\n    color: #475569;\n    font-weight: 500;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Metrics Section -->\n  <div class=\"dg-metrics-container\">\n    <div class=\"dg-metric-box\">\n      <span class=\"dg-metric-value\">15%<\/span>\n      <span class=\"dg-metric-label\">fewer parameters than comparable Transformer models.<\/span>\n    <\/div>\n    \n    <div class=\"dg-metric-box\">\n      <span class=\"dg-metric-value\">18%<\/span>\n      <span class=\"dg-metric-label\">fewer floating-point operations (FLOPs) per image.<\/span>\n    <\/div>\n    \n    <div class=\"dg-metric-box\">\n      <span class=\"dg-metric-value\">&times;4<\/span>\n      <span class=\"dg-metric-label\">Validated at &times;4 enhancement scale, alongside &times;2 and &times;3.<\/span>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway-1024x768.jpg\" alt=\"Runway image comparison using the UCMerced dataset, highlighting edge preservation.\" class=\"wp-image-3412\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway-1024x768.jpg 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway-300x225.jpg 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway-1536x1152.jpg 1536w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway-768x576.jpg 768w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Runway.jpg 1953w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Runway image comparison using the UCMerced dataset, highlighting edge preservation.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"768\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass-1024x768.jpg\" alt=\"\" class=\"wp-image-3413\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass-1024x768.jpg 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass-300x225.jpg 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass-1536x1152.jpg 1536w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass-768x576.jpg 768w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/Overpass.jpg 1953w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Overpass image comparison using the UCMerced dataset, highlighting structural detail.<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udccc Applications<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nRemote sensing image enhancement can support multiple satellite-service markets where clearer structures improve downstream visual interpretation and analysis.\n<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor-1024x576.jpg\" alt=\"\" class=\"wp-image-3415\" srcset=\"https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor-1024x576.jpg 1024w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor-768x432.jpg 768w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor-1536x864.jpg 1536w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor-300x169.jpg 300w, https:\/\/dgway.com\/blog_E\/wp-content\/uploads\/2026\/08\/flood-monitor.jpg 1672w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Flood monitoring with HybridATNet-enhanced imagery.<\/figcaption><\/figure>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Applications Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n    color: #334155;\n    line-height: 1.6;\n  }\n\n  .dg-app-grid {\n    display: grid;\n    \/* \u0e08\u0e31\u0e14\u0e40\u0e1b\u0e47\u0e19 2 \u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e1a\u0e19 Desktop, 1 \u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e1a\u0e19 Mobile *\/\n    grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));\n    gap: 24px;\n    margin-bottom: 48px;\n  }\n\n  .dg-app-card {\n    background-color: #ffffff;\n    border: 1px solid #bae6fd; \/* \u0e01\u0e23\u0e2d\u0e1a\u0e2a\u0e35\u0e1f\u0e49\u0e32\u0e2d\u0e48\u0e2d\u0e19 *\/\n    border-radius: 8px;\n    padding: 28px 24px;\n    box-shadow: 0 2px 8px rgba(2, 132, 199, 0.04);\n    transition: transform 0.2s ease, box-shadow 0.2s ease;\n  }\n\n  .dg-app-card:hover {\n    transform: translateY(-2px);\n    box-shadow: 0 6px 16px rgba(2, 132, 199, 0.08);\n  }\n\n  .dg-app-title {\n    margin-top: 0;\n    margin-bottom: 12px;\n    font-size: 1.8rem;\n    color: #0369a1; \/* \u0e2a\u0e35\u0e19\u0e49\u0e33\u0e40\u0e07\u0e34\u0e19\u0e40\u0e02\u0e49\u0e21\u0e43\u0e2b\u0e49\u0e2b\u0e31\u0e27\u0e02\u0e49\u0e2d\u0e40\u0e14\u0e48\u0e19 *\/\n    font-weight: 700;\n  }\n\n  .dg-app-text {\n    margin: 0;\n    font-size: 1.4rem;\n    color: #475569;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Applications Section -->\n  <div class=\"dg-app-grid\">\n    <!-- Card 1 -->\n    <div class=\"dg-app-card\">\n      <h4 class=\"dg-app-title\">Disaster Response<\/h4>\n      <p class=\"dg-app-text\">Support faster visual assessment of flood zones, damaged infrastructure, blocked roads, and affected residential areas after major events.<\/p>\n    <\/div>\n    \n    <!-- Card 2 -->\n    <div class=\"dg-app-card\">\n      <h4 class=\"dg-app-title\">Infrastructure Monitoring<\/h4>\n      <p class=\"dg-app-text\">Improve visibility of roads, bridges, ports, airports, runways, and industrial facilities for inspection, planning, and change detection workflows.<\/p>\n    <\/div>\n    \n    <!-- Card 3 -->\n    <div class=\"dg-app-card\">\n      <h4 class=\"dg-app-title\">Agriculture &amp; Land Management<\/h4>\n      <p class=\"dg-app-text\">Reveal clearer field boundaries, land-use transitions, irrigation layouts, and crop-region structures for agricultural intelligence services.<\/p>\n    <\/div>\n    \n    <!-- Card 4 -->\n    <div class=\"dg-app-card\">\n      <h4 class=\"dg-app-title\">Environmental Monitoring<\/h4>\n      <p class=\"dg-app-text\">Assist analysis of forests, coastlines, wetlands, rivers, and natural boundary changes where image clarity improves long-term monitoring value.<\/p>\n    <\/div>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\u2705 Final Takeaway<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">HybridATNet provides an AI-based path to improve the visual quality of remote sensing imagery without requiring immediate sensor replacement. By combining CNN-based local feature extraction, Transformer-based global understanding, and hybrid feature refinement, the approach is designed to improve image detail while maintaining computational efficiency.<\/p>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udcda Research Background<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nHybridATNet is based on peer-reviewed research developed at the <strong>Multimedia Data Analytics and Processing Research Unit, Chulalongkorn University, Thailand<\/strong>.\n<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><br>N. Sultan, W. Ruangsang, and S. Aramvith, \u201cHybridATNet: Multi-Scale Attention and Hybrid Feature Refinement Network for Remote Sensing Image Super-Resolution,\u201d <em>IEEE Access<\/em>, vol. 13, pp. 159979\u2013159997, 2025.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Published 9 September 2025 \u00b7 DOI: 10.1109\/ACCESS.2025.3608009 \u00b7 Open access (CC BY 4.0)<\/p>\n<\/blockquote>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Buttons Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n  }\n\n  .dg-button-group {\n    display: flex;\n    flex-wrap: wrap;\n    gap: 16px;\n    margin-top: 32px;\n    margin-bottom: 32px;\n  }\n  \n  .dg-btn {\n    display: inline-flex;\n    align-items: center;\n    justify-content: center;\n    padding: 12px 28px;\n    border-radius: 9999px; \n\n    font-weight: 600;\n    font-size: 1.8rem;\n    text-decoration: none;\n    transition: all 0.2s ease;\n    cursor: pointer;\n  }\n  \n  \/* Primary Solid Button (Orange) *\/\n  .dg-btn-primary {\n    background-color: #f4a51c; \n    color: #0f172a !important; \n    border: 2px solid #f4a51c;\n  }\n  \n  .dg-btn-primary:hover {\n    background-color: #f4a51c;\n    color: #ffffff !important;\n    border-color: #f4a51c;\n  }\n  \n  \/* Secondary Outline Button *\/\n  .dg-btn-secondary {\n    background-color: #5cc4ed !important;\n    color: #ffffff !important; \n    border: 2px solid #5cc4ed !important;\n  }\n  \n  .dg-btn-secondary:hover {\n    background-color: transparent !important; \n    color: #000000 !important;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Action Buttons -->\n  <div class=\"dg-button-group\">\n    <a href=\"https:\/\/ieeexplore.ieee.org\/document\/11153914\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"dg-btn dg-btn-primary\">\n      Read the full paper on IEEE Xplore\n    <\/a>\n    \n    <!-- \u0e1b\u0e38\u0e48\u0e21 Discuss -->\n    <a href=\"https:\/\/dgway.com\/contact.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"dg-btn dg-btn-secondary\">\n      Discuss your use case\n    <\/a>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 24 24\">\n  <path d=\"M7.8 6c0-.7.6-1.2 1.2-1.2h6c.7 0 1.2.6 1.2 1.2v3h1.5V6c0-1.5-1.2-2.8-2.8-2.8H9C7.5 3.2 6.2 4.5 6.2 6v3h1.5V6zm8.4 11c0 .7-.6 1.2-1.2 1.2H9c-.7 0-1.2-.6-1.2-1.2v-3H6.2v3c0 1.5 1.2 2.8 2.8 2.8h6c1.5 0 2.8-1.2 2.8-2.8v-3h-1.5v3zM4 11v1h16v-1z\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd17Learn More &amp; Official Links<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\nExplore official resources and related Design Gateway content for more information about CUEE MDAP Lab research and FPGA &amp; AI collaboration.\n<\/p>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Buttons Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n  }\n\n  .dg-button-group {\n    display: flex;\n    flex-wrap: wrap;\n    gap: 16px;\n    margin-top: 32px;\n    margin-bottom: 32px;\n  }\n  \n  .dg-btn {\n    display: inline-flex;\n    align-items: center;\n    justify-content: center;\n    padding: 12px 28px;\n    border-radius: 9999px; \n\n    font-weight: 600;\n    font-size: 1.8rem;\n    text-decoration: none;\n    transition: all 0.2s ease;\n    cursor: pointer;\n  }\n  \n  \/* Primary Solid Button (Orange) *\/\n  .dg-btn-primary_1 {\n    background-color: #89eb73; \n    color: #ffffff; \n    border: 2px solid #89eb73;\n  }\n  \n  .dg-btn-primary_1:hover {\n    background-color: #89eb73;\n    color: #ffffff;\n    border-color: #89eb73;\n  }\n  \n    \/* Secondary Outline Button *\/\n  .dg-btn-secondary {\n    background-color: #5cc4ed !important;\n    color: #ffffff !important; 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AI articles on Design Gateway&#8217;s Blog\n    <\/a>\n  <\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 20 20\">\n  <path d=\"M18 12h-2.18c-.17.7-.44 1.35-.81 1.93l1.54 1.54-2.1 2.1-1.54-1.54c-.58.36-1.23.63-1.91.79V19H8v-2.18c-.68-.16-1.33-.43-1.91-.79l-1.54 1.54-2.12-2.12 1.54-1.54c-.36-.58-.63-1.23-.79-1.91H1V9.03h2.17c.16-.7.44-1.35.8-1.94L2.43 5.55l2.1-2.1 1.54 1.54c.58-.37 1.24-.64 1.93-.81V2h3v2.18c.68.16 1.33.43 1.91.79l1.54-1.54 2.12 2.12-1.54 1.54c.36.59.64 1.24.8 1.94H18zm-8.5 1.5c1.66 0 3-1.34 3-3s-1.34-3-3-3-3 1.34-3 3 1.34 3 3 3\"><\/path>\n<\/svg><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udce9 Contact Us<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Interested in collaborating with or learning more about research from the <strong>CUEE MDAP Lab<\/strong> \u2014 including <strong>HybridATNet<\/strong> and related work in areas such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Anomaly Detection<\/li>\n\n\n\n<li>Digital Image and Video Super-Resolution Techniques<\/li>\n\n\n\n<li>Digital Video Coding<\/li>\n\n\n\n<li>Face Recognition and Emotional Expression<\/li>\n\n\n\n<li>Remote Sensing Image Enhancement<\/li>\n\n\n\n<li>Medical &#8211; stereo endoscopic SR, Brain MRI reviews<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Please reach out via <strong>Design Gateway<\/strong> to connect with the research team. \ud83d\ude80<\/p>\n\n\n\n<style>\n  \/* Minimal Blue Theme - Buttons Section *\/\n  .dg-blog-wrapper {\n    font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen-Sans, Ubuntu, Cantarell, \"Helvetica Neue\", sans-serif;\n  }\n \n  .dg-btn {\n    display: inline-flex;\n    align-items: center;\n    justify-content: center;\n    padding: 12px 28px;\n    border-radius: 9999px; \n\n    font-weight: 600;\n    font-size: 1.8rem;\n    text-decoration: none;\n    transition: all 0.2s ease;\n    cursor: pointer;\n  }\n  \n  \n  \/* Secondary Outline Button (Blue) *\/\n  .dg-btn-secondary {\n    background-color: #89eb73;\n    color: #ffffff; \n    border: 2px solid #89eb73;\n  }\n  \n  .dg-btn-secondary:hover {\n    background-color: #89eb73; \n    color: #ffffff;\n  }\n<\/style>\n\n<div class=\"dg-blog-wrapper\">\n  <!-- Action Buttons -->\n  \n    <!-- \u0e1b\u0e38\u0e48\u0e21 Discuss -->\n    <a href=\"https:\/\/dgway.com\/contact.html\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"dg-btn dg-btn-secondary\">\n      Contact Design Gateway today\n    <\/a>\n  <\/div>\n\n\n\n<div class=\"wp-block-boldblocks-icon-separator\" style=\"--bb--margin-top--sm:var(--bb--margin-top--lg);--bb--margin-bottom--sm:var(--bb--margin-bottom--lg);--bb--margin-top--md:var(--bb--margin-top--lg);--bb--margin-bottom--md:var(--bb--margin-bottom--lg);--bb--margin-top--lg:0;--bb--margin-bottom--lg:0\" role=\"separator\"><div class=\"wp-block-boldblocks-icon-separator__inner icon-center is-align-center\" style=\"--bb-border:1px solid;--bb--icon-width:1em;--bb--icon-spacing:.5em\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 20 20\">\n  <path d=\"M18 12h-2.18c-.17.7-.44 1.35-.81 1.93l1.54 1.54-2.1 2.1-1.54-1.54c-.58.36-1.23.63-1.91.79V19H8v-2.18c-.68-.16-1.33-.43-1.91-.79l-1.54 1.54-2.12-2.12 1.54-1.54c-.36-.58-.63-1.23-.79-1.91H1V9.03h2.17c.16-.7.44-1.35.8-1.94L2.43 5.55l2.1-2.1 1.54 1.54c.58-.37 1.24-.64 1.93-.81V2h3v2.18c.68.16 1.33.43 1.91.79l1.54-1.54 2.12 2.12-1.54 1.54c.36.59.64 1.24.8 1.94H18zm-8.5 1.5c1.66 0 3-1.34 3-3s-1.34-3-3-3-3 1.34-3 3 1.34 3 3 3\"><\/path>\n<\/svg><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Improving satellite image quality can follow two paths. The conventional path depends on new hardware investment, while the HybridATNet path enhances the value of images already captured by existing sensors. Developed from research at Chulalongkorn University, HybridATNet applies AI-based super-resolution to improve the clarity and usefulness of remote sensing imagery. \ud83d\udd0d Overview For satellite service providers and remote sensing users,&#46;&#46;&#46;<\/p>\n","protected":false},"author":1,"featured_media":3415,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[1059,1034,127,271,40,238,144,1058,1061],"class_list":["post-3389","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fpga-ai","tag-ai","tag-chulalongkorn-university","tag-design-gateway","tag-edge-computing","tag-fpga","tag-hardware-acceleration","tag-high-performance-computing","tag-hybridatnet","tag-remote-sensing-image"],"_links":{"self":[{"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/posts\/3389","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/comments?post=3389"}],"version-history":[{"count":46,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/posts\/3389\/revisions"}],"predecessor-version":[{"id":3445,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/posts\/3389\/revisions\/3445"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/media\/3415"}],"wp:attachment":[{"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/media?parent=3389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/categories?post=3389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dgway.com\/blog_E\/wp-json\/wp\/v2\/tags?post=3389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}