Papers › Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang
Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | BSD100 - 4x upscaling | LapSRN | PSNR | 27.32 | #46 of 71 | Archive leaderboard | report |
| Image Super-Resolution | BSD100 - 4x upscaling | LapSRN | SSIM | 0.728 | #46 of 71 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 4x upscaling | LapSR | PSNR | 28.19 | #77 of 104 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 4x upscaling | LapSR | SSIM | 0.772 | #77 of 104 | Archive leaderboard | report |
| Image Super-Resolution | Urban100 - 4x upscaling | LapSRN | PSNR | 25.21 | #55 of 65 | Archive leaderboard | report |
| Image Super-Resolution | Urban100 - 4x upscaling | LapSRN | SSIM | 0.756 | #55 of 65 | 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.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections