Denoising image by matrix factorization in U-shaped convolutional neural network.

Image denoising requires both spatial details and global contextualized information to recover a clean version from the deteriorative one. Previous deep convolution networks usually focus on modeling the local feature and stacked convolution blocks to expand the receptive field, which can catch the long-distance dependencies. However, contrary to the expectation, the extracted local feature incapacity recovers the global details by traditional convolution while the stacked blocks hinder the information flow. To tackle these issues, we introduce the M atrix Factorization D enoising Module (MD) to model the interrelationship between the global context aggregating process and the reconstructed process to attain the context details. Besides, we redesign a new basic block to ease the information flow and maintain the network performance. In addition, we conceive the F eature Fu sion Module (FFU) to fuse the information from the different sources. Inspired by the multi-stage progressive restoration architecture, we adopt two-stage convolution branches progressively reconstructing the denoised image. In this paper, we propose an original and efficient neural convolution network dubbed MFU. Experimental results on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise datasets show that our MFU can produce comparable visual quality and accuracy results with state-of-the-art methods. • We propose MFU, an effective and efficient end-to-end multi-stage progressive convolution network with a new basic block for image denoising. • We represent the global structured information as the low-rank matrix and use the MD module to learn the contextually-enriched and globally structured feature. • Designing a module (FFU) fuses the multi-source information to promote the network reconstruct the image. [ABSTRACT FROM AUTHOR]