Papers › Densely Residual Laplacian Super-Resolution

Densely Residual Laplacian Super-Resolution

28 Jun 2019arXiv:1906.12021archive 2025-07-28

Saeed Anwar, Nick Barnes

Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately.

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saeed-anwar/DRLN mentioned on GitHubpytorchMIT report

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calc_psnr saeed-anwar/DRLN/TestCode/code/utility.py community (archive-listed) ran MIT (permissive) · 0f13790730dde821 · report
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Tasks

Image Super-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Super-Resolution BSD100 - 2x upscaling DRLN+ PSNR 32.47 #14 of 30 Archive leaderboard report
Image Super-Resolution BSD100 - 2x upscaling DRLN+ SSIM 0.9032 #14 of 30 Archive leaderboard report
Image Super-Resolution BSD100 - 3x upscaling DRLN+ PSNR 29.4 #10 of 21 Archive leaderboard report
Image Super-Resolution BSD100 - 3x upscaling DRLN+ SSIM 0.8125 #10 of 21 Archive leaderboard report
Image Super-Resolution BSD100 - 4x upscaling DRLN+ PSNR 27.87 #12 of 71 Archive leaderboard report
Image Super-Resolution BSD100 - 4x upscaling DRLN+ SSIM 0.7453 #12 of 71 Archive leaderboard report
Image Super-Resolution BSD100 - 8x upscaling DRLN+ PSNR 25.06 #1 of 6 Archive leaderboard report
Image Super-Resolution BSD100 - 8x upscaling DRLN+ SSIM 0.607 #1 of 6 Archive leaderboard report
Image Super-Resolution Manga109 - 2x upscaling DRLN+ PSNR 39.75 #11 of 21 Archive leaderboard report
Image Super-Resolution Manga109 - 2x upscaling DRLN+ SSIM 0.9792 #11 of 21 Archive leaderboard report
Image Super-Resolution Manga109 - 3x upscaling DRLN+ PSNR 34.94 #9 of 17 Archive leaderboard report
Image Super-Resolution Manga109 - 3x upscaling DRLN+ SSIM 0.9518 #9 of 17 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling DRLN+ PSNR 31.78 #20 of 50 Archive leaderboard report
Image Super-Resolution Manga109 - 4x upscaling DRLN+ SSIM 0.9211 #20 of 50 Archive leaderboard report
Image Super-Resolution Manga109 - 8x upscaling DRLN+ PSNR 25.55 #2 of 5 Archive leaderboard report
Image Super-Resolution Manga109 - 8x upscaling DRLN+ SSIM 0.8087 #2 of 5 Archive leaderboard report
Image Super-Resolution Set14 - 2x upscaling DRLN+ PSNR 34.43 #12 of 35 Archive leaderboard report
Image Super-Resolution Set14 - 2x upscaling DRLN+ SSIM 0.9247 #12 of 35 Archive leaderboard report
Image Super-Resolution Set14 - 3x upscaling DRLN+ PSNR 30.8 #10 of 24 Archive leaderboard report
Image Super-Resolution Set14 - 3x upscaling DRLN+ SSIM 0.8498 #10 of 24 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling DRLN+ PSNR 29.02 #29 of 104 Archive leaderboard report
Image Super-Resolution Set14 - 4x upscaling DRLN+ SSIM 0.7914 #29 of 104 Archive leaderboard report
Image Super-Resolution Set14 - 8x upscaling DRLN+ PSNR 25.4 #2 of 7 Archive leaderboard report
Image Super-Resolution Set14 - 8x upscaling DRLN+ SSIM 0.6547 #2 of 7 Archive leaderboard report
Image Super-Resolution Set5 - 2x upscaling DRLN+ PSNR 38.34 #14 of 41 Archive leaderboard report
Image Super-Resolution Set5 - 2x upscaling DRLN+ SSIM 0.9619 #14 of 41 Archive leaderboard report
Image Super-Resolution Set5 - 3x upscaling DRLN+ PSNR 34.86 #13 of 32 Archive leaderboard report
Image Super-Resolution Set5 - 3x upscaling DRLN+ SSIM 0.9307 #13 of 32 Archive leaderboard report
Image Super-Resolution Set5 - 8x upscaling DRLN+ PSNR 27.46 #4 of 8 Archive leaderboard report
Image Super-Resolution Set5 - 8x upscaling DRLN+ SSIM 0.7916 #4 of 8 Archive leaderboard report
Image Super-Resolution Urban100 - 2x upscaling DRLN+ PSNR 33.54 #11 of 29 Archive leaderboard report
Image Super-Resolution Urban100 - 2x upscaling DRLN+ SSIM 0.9402 #11 of 29 Archive leaderboard report
Image Super-Resolution Urban100 - 3x upscaling DRLN+ PSNR 29.37 #11 of 22 Archive leaderboard report
Image Super-Resolution Urban100 - 3x upscaling DRLN+ SSIM 0.8746 #11 of 22 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling DRLN+ PSNR 27.14 #20 of 65 Archive leaderboard report
Image Super-Resolution Urban100 - 4x upscaling DRLN+ SSIM 0.8149 #20 of 65 Archive leaderboard report
Image Super-Resolution Urban100 - 8x upscaling DRLN+ PSNR 23.24 #1 of 5 Archive leaderboard report
Image Super-Resolution Urban100 - 8x upscaling DRLN+ SSIM 0.6523 #1 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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