Deep Residual Network for MSFA Raw Image Denoising

Abstract

Multispectral filter arrays (MSFA) is increasingly used in multispectral imaging. While many previous works studied the denoising algorithms for CFA based cameras, denoising MSFA raw images is little discussed. The major challenges for denoising MSFA data include 1) more channels than CFA and no predominant channel; 2) compatibility between denoising and the subsequent demosaicking process. To overcome these challenges, we propose a new deep residual network designed to account for the uniqueness of MSFA mosaic patterns. First, a split and stride convolution layer is innovated to match the mosaic pattern of the MSFA raw image. Then, data augmentation using MSFA shifting and dynamic noise is proposed to make the model robust to different noise levels. In addition, a new network optimization criteria is also suggested by using the noise standard deviation to normalize the L1 loss function. Comprehensive experiments demonstrate that the proposed deep residual network outperforms the state-of-the-art denoising algorithms in MSFA field.