From 6c83613ec25a29c5acd7a65d53089869923b056b Mon Sep 17 00:00:00 2001
From: unknown <13915959820@163.com>
Date: Mon, 27 May 2024 13:45:49 +0800
Subject: [PATCH] 202405271345

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 index.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/index.html b/index.html
index 55ac9d8..f0272f0 100644
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@@ -182,10 +182,10 @@ <h3 align="left">Recently Published</h3><br />
                     <i class="fa fa-quote-left"></i> <a href="javascript:toggleblock('ZhangIGARSS2024Bib')">BibTeX</a> &nbsp;
                     <!-- <i class="fa fa-github"></i> <a href="" target="_blank">Code</a> -->
                 </p>
-                <p id="ZhangTGRS2024Abs" class="abstract" style="display: none;">
+                <p id="ZhangIGARSS2024Abs" class="abstract" style="display: none;">
                     Satellite video images contain temporal contextual information that is unavailable in single-frame images. Therefore, using a sequence of frames for super-resolution can significantly enhance the reconstruction effect. However, most existing satellite Video Super-Resolution (VSR) methods focus on improving the network’s presentation ability, overlooking the complex degradation processes present in real-world satellite videos which appear as a blind SR problem. In this paper, we propose an effective satellite VSR method based on a unidirectional recurrent network named URD-VSR. Simultaneously, a network independent of the SR structure is utilized to model the degradation process. Experiments on real satellite video datasets and integration with object detection demonstrate the effectiveness of the proposed method.
                 </p>
-                <pre xml:space="preserve" id="ZhangTGRS2024Bib" class="bibtex" style="display: none;">
+                <pre xml:space="preserve" id="ZhangIGARSS2024Bib" class="bibtex" style="display: none;">
 Coming Soon
                 </pre>
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