Cascade Convolutional Neural Network for Image Super-Resolution
With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution. Previous works mainly focus on optimizing the structure of SRCNN, which have been achieved well performance in speed and restoration quality for image super-resolution. However, most of these approaches only consider a specific scale image during the training process, while ignoring the relationship between different scales of images. Motivated by this concern, in this paper, we propose a cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast SRCNN can process a specific scale image. Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images. Extensive experiments show that our network can achieve well performance for image SR.
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Datasets
BSD
Set14
Set5
General-100
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Image Super-Resolution | BSD200 - 2x upscaling | CSRCNN | PSNR | 32.92 | # 1 | |
| SSIM | 0.9122 | # 1 | ||||
| Image Super-Resolution | Set14 - 2x upscaling | CSRCNN | PSNR | 34.34 | # 6 | |
| SSIM | 0.9240 | # 6 | ||||
| Image Super-Resolution | Set14 - 4x upscaling | CSRCNN | PSNR | 28.47 | # 44 | |
| SSIM | 0.7720 | # 57 | ||||
| Image Super-Resolution | Set14 - 8x upscaling | CSRCNN | PSNR | 24.30 | # 6 | |
| SSIM | 0.614 | # 6 | ||||
| Image Super-Resolution | Set5 - 2x upscaling | CSRCNN | PSNR | 37.45 | # 27 | |
| SSIM | 0.9570 | # 15 | ||||
| Image Super-Resolution | Set5 - 8x upscaling | CSRCNN | PSNR | 25.74 | # 8 | |
| SSIM | 0.715 | # 7 |
