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SRCNN (TPAMI'2015)

Image Super-Resolution Using Deep Convolutional Networks

Task: Image Super-Resolution

Abstract

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Results and models

Evaluated on RGB channels, scale pixels in each border are cropped before evaluation. The metrics are PSNR / SSIM .

Model Dataset PSNR SSIM Training Resources Download
srcnn_x4k915_1x16_1000k_div2k Set5 28.4316 0.8099 1 model | log
srcnn_x4k915_1x16_1000k_div2k Set14 25.6486 0.7014 1 model | log
srcnn_x4k915_1x16_1000k_div2k DIV2K 27.7460 0.7854 1 model | log

Quick Start

Train

Train Instructions

You can use the following commands to train a model with cpu or single/multiple GPUs.

# cpu train
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py

# single-gpu train
python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py

# multi-gpu train
./tools/dist_train.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py 8

For more details, you can refer to Train a model part in train_test.md.

Test

Test Instructions

You can use the following commands to test a model with cpu or single/multiple GPUs.

# cpu test
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth

# single-gpu test
python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth

# multi-gpu test
./tools/dist_test.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 8

For more details, you can refer to Test a pre-trained model part in train_test.md.

Citation

@article{dong2015image,
  title={Image super-resolution using deep convolutional networks},
  author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={38},
  number={2},
  pages={295--307},
  year={2015},
  publisher={IEEE}
}