Real Image Denoising Based on Multi-Scale Residual Dense Block and Cascaded U-Net With Block-Connection

Long Bao, Zengli Yang, Shuangquan Wang, Dongwoon Bai, Jungwon Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 448-449

Benefiting from the recent real image dataset, learning-based approaches have achieved good performance for real-image denoising. To further improve the performance for Bayer raw data denoising, this paper introduces two new networks, which are multi-scale residual dense network (MRDN) and multi-scale residual dense cascaded U-Net with block-connection (MCU-Net). Both networks are built upon a newly designed multi-scale residual dense block (MRDB), and MCU-Net uses MRDB to connect the encoder and decoder of the U-Net. To better exploit the multi-scale feature of the images, the MRDB adds another branch of atrous spatial pyramid pooling (ASPP) based on residual dense block (RDB). Compared to the skip connection, the block-connection using MRDB can adaptively transform the features of the encoder and transfer them to the decoder of the U-Net. In addition, a novel noise permutation algorithm is introduced to avoid model overfitting. The superior performance of these new networks in removing noise within Bayer images has been demonstrated by comparison results on the SIDD benchmark, and the top ranking of SSIM in the NTIRE 2020 Challenge on Real Image Denoising - Track1: rawRGB.