Robust Low-Rank Convolution Network for Image Denoising
Jiahuan Ren, Zhao Zhang, Richang Hong, Mingliang Xu, Haijun Zhang, Mingbo Zhao, Meng Wang
Abstract
Convolutional Neural Networks (CNNs) are powerful for image representation, but the convolution operation may be influenced and degraded by the included noise, and the deep features may not be fully learned. In this paper, we propose a new encoder-decoder based image restoration network, termed Robust Low-Rank Convolution Network with Feature Denoising (LRCnet). LRCnet presents a novel low-rank convolution (LR-Conv) for image representation, and a residual dense connection (RDC) for feature fusion between encoding and decoding. Different from directly splitting convolution into ordinary convolution and mirror convolution as existing work, LR-Conv deploys a feature denoising module after the ordinary convolution to remove noise for mirror convolution. A low-rank embedding process is then used to project the convolutional features into a robust low-rank subspace, which can retain the local geometry of input signal to some extent and separate the signal and noise by finding low-rank structure of features to reduce the impact of noise on convolution. Besides, most networks increase the depth of network simply to obtain deep information and lack of effective connections to fuse the multilevel features, which may not fully discover the deep features in various layers. Thus, we design a residual dense connection with a channel attention to connect multilevel feature effectively to obtain more useful information to enhance the data representation. Extensive experiments on several datasets verified the effectiveness of LRCnet for image denoising.