DRNet: A Deep Neural Network With Multi-Layer Residual Blocks Improves Image Denoising

Abstract

Recently, with the broad applications of deep learning technology in image denoising, many deep neural networks based on the residual block (ResBlock) have been proposed to improve the performance. This paper presents DC-ResBlock, a ResBlock with an extra dilated convolution, to replace standard ResBlock for image denoising. Our study shows that the deep neural network with DC-ResBlocks, named DRNet, can achieve a very competitive result. We experiment with other residual blocks by adding Rectified Linear Unit, batch normalization, and Dilated Convolution to the standard ResBlock in several different ways. Ablation study shows that DC-Resblock has the best denoising performance. To evaluate the robustness of proposed DRNet, we make a statistical measure using many different random seeds instead of just a single seed, as used by many previous studies. DRNet performs well in denoising gray and color images with additive white Gaussian noise. Furthermore, because of its good performance on the Smartphone Image Denoising Dataset, DRNet is hopeful to be applied in practical tasks. The code of DRNet is accessible at https://github.com/JiaHongZ/DRNet.

Keywords

• Image denoising
• convolutional neural networks
• residual learning