Review: WDRN / WavResNet — Wavelet-based Deep Residual Learning Network (Image Denoising & Super Resolution)

Wavelet Transformed Image As Input, Outperforms VDSR, DnCNN, Ranked Third in NTIRE Competition

Outline

1. Network Architecture

The Four Patches as Input After Wavelet Transform
Network Architecture

1. The input feature space is mapped to another feature space which can be trained easier, which can help to reduce the network depth, i.e. reduce the computational complexity. Also, it is easier to be trained.

2. The patch size can be reduced by half. It can reduce the runtime of the network due to the size of the output images of layers being halved.

3. The minimum required size of receptive field can be reduced.

NTIRE SISR Competition Architecture
Average PSNR/SSIM on 50 validation data of DIV2K dataset
PSNR on “Set12” dataset in the Gaussian denoising task
SSIM on “Set12” dataset in the Gaussian denoising task
Average PSNR/SSIM for “BSD68” dataset in the Gaussian denoising task
Visual Quality
PSNR/SSIM for various datasets in SISR tasks (Proposed-P is trained using 291 dataset, and Proposed is trained using RGB of DIV2K dataset)
Performance comparison of SISR at scale factor of ×4 of bicubic downsampling (Left : input, Center : restoration result, Right : label.)
Performance comparison of SISR at scale factor of ×4 of unknown downsampling. (Left : input, Center : restoration result, Right : label.)

During the days of coronavirus, I hope to write 30 stories in this month to give myself a small challenge. And this is the 32nd story in this month. Thanks for visiting my story…

2 Days left for this month. How about 35 stories within this month…?

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