Caffe_VDSR

This is a implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" (CVPR 2016 Oral Paper) in caffe.

Instruction

VDSR (Very Deep network for Super-Resolution) is an end-to-end network with 20 convolution layers for single image super-resolution. The performance of VDSR is better than other state-of-the-art SISR methods, such as SRCNN, A+ and CSCN (My implementation of CSCN).

Dependencies

Train

• Caffe

Test

• MatConvNet

Usage

Train

1. Place the "Train" folder into "($Caffe_Dir)/examples/", and rename "Train" to "VDSR"

2. Open MATLAB and direct to ($Caffe_Dir)/example/VDSR, run "generate_train.m" and "generate_test.m" to generate training and test data. (Code from SRCNN)

3. To train VDSR, run ./build/tools/caffe train --solver examples/VDSR/VDSR_solver.prototxt

4. Set clip_gradients in VDSR_solver.prototxt to solve gradient explosion problem, 0.1 or 1 is a good choice

5. Change the learning rate when the error plateaus

Test

1. "Demo_SR_Conv.m" is a simple test code. Just run it and you will get the result.

2. "VDSR_170000.mat" is a model trained by myself.

Different from original paper

Because of the limitation of hardware conditions，I didn't do completed training. So there are some differences between this implementation and original paper.

Training Dataset

This implementation: 91 images (with data augumentation and only factor 2)

Original paper: 291 images (with data augumentation and factor 2, 3 and 4)

Multi scale

This implementation: Casade of 2x to generate 3x and 4x result

Original paper: Multi scale in one model

Training Time of Final Model

This implementation: about 30 epoch

Original paper: about 80 epoch

Performance in PSNR

Factor 2

DataSet	Original	This Implementation
Set5	37.53	37.46
Set14	33.03	32.83
BSD100	31.90	31.65

Factor 3

DataSet	Original	This Implementation
Set5	33.66	33.52
Set14	29.77	29.55
BSD100	28.82	28.62

Factor 4

DataSet	Original	This Implementation
Set5	31.35	31.14
Set14	28.01	27.81
BSD100	27.29	27.13

TODO

1. Add Super-resolution image results
2. Complete experiments

References

Please cite [1] if you use this code in your work, thank you!

[1] Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015