Papers › Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network...
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
Jin Yamanaka, Shigesumi Kuwashima, Takio Kurita
We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. Current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and is not suitable for network edge devices like mobile, tablet and IoT devices. Our model achieves state of the art reconstruction performance with at least 10 times lower calculation cost by Deep CNN with Residual Net, Skip Connection and Network in Network (DCSCN). A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area. Parallelized 1x1 CNNs, like the one called Network in Network, is also used for image reconstruction. That structure reduces the dimensions of the previous layer's output for faster computation with less information loss, and make it possible to process original images directly. Also we optimize the number of layers and filters of each CNN to significantly reduce the calculation cost. Thus, the proposed algorithm not only achieves the state of the art performance but also achieves faster and efficient computation. Code is available at https://github.com/jiny2001/dcscn-super-resolution
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | Set14 - 2x upscaling | DCSCN | PSNR | 33.05 | #29 of 35 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 2x upscaling | DCSCN | SSIM | .9126 | #29 of 35 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 2x upscaling | c-DCSCN | PSNR | 32.71 | #34 of 35 | Archive leaderboard | report |
| Image Super-Resolution | Set14 - 2x upscaling | c-DCSCN | SSIM | .9090 | #34 of 35 | Archive leaderboard | report |
| Image Super-Resolution | Set5 - 2x upscaling | c-DCSCN | PSNR | 37.13 | #39 of 41 | Archive leaderboard | report |
| Image Super-Resolution | Set5 - 2x upscaling | c-DCSCN | SSIM | .9569 | #39 of 41 | 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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