Search

Residual learning is one of the most effective components in blind image denoising. It learns to estimate the noise instead of the clean image itself. A shortcoming of residual learning is that it cannot capture hierarchical features efficiently because there are no connections between layers to extract multi-level features. Therefore, residual dense network (RDN) adds dense connections among layers in the residual block to facilitate extraction of hierarchical features. Although RDN is a superior non-blind denoiser, it is only better than state-of-the-art methods in blind denoising when noise level is high. In this paper, we employ the concept of Wiener filters and add a non-residual network as a guidance for RDN. The non-residual network predicts the signal instead of the noise to assist RDN in achieving satisfactory performance across all noise levels. Then, a guidance network combines the outputs from both residual and non-residual networks to generate the final denoised output. Experimental results show that the new architecture performs better than state-of-the-art blind denoisers quantitatively and image quality of the new method is also better than existing methods. • Improve performance of residual dense network in blind image denoising by employing the concept of Wiener filters. • Use non-residual network as a guidance of residual dense network and assist in predicting the signal instead of the noise. • Multiplications of noisy input and outputs from networks are used for signal and noise power prediction. • A guidance network combines input, outputs from networks, and the above multiplications to generate final denoised output. • Experimental results show residual dense network with non-residual guidance performs better than state-of-the-art methods. [ABSTRACT FROM AUTHOR]