Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution

Abstract

Convolutional Neural Network (CNN)-based image super-resolution (SR) has exhibited impressive success on known degraded low-resolution (LR) images. However, this type of approach is hard to hold its performance in practical scenarios when the degradation process (i.e. blur and downsampling) is unknown. Despite existing blind SR methods proposed to solve this problem using blur kernel estimation, the perceptual quality and reconstruction accuracy are still unsatisfactory. In this paper, we analyze the degradation of a high-resolution (HR) image from image intrinsic components according to a degradation-based formulation model. We propose a components decomposition and co-optimization network (CDCN) for blind SR. Firstly, CDCN decomposes the input LR image into structure and detail components in feature space. Then, the mutual collaboration block (MCB) is presented to exploit the relationship between both two components. In this way, the detail component can provide informative features to enrich the structural context and the structure component can carry structural context for better detail revealing via a mutual complementary manner. After that, we present a degradation-driven learning strategy to jointly supervise the HR image detail and structure restoration process. Finally, a multi-scale fusion module followed by an upsampling layer is designed to fuse the structure and detail features and perform SR reconstruction. Empowered by such degradation-based components decomposition, collaboration, and mutual optimization, we can bridge the correlation between component learning and degradation modelling for blind SR, thereby producing SR results with more accurate textures. Extensive experiments on both synthetic SR datasets and realworld images show that the proposed method achieves the stateof-the-art performance compared to existing methods.

DIRBW-Net: An Improved Inverted Residual Network Model for Underwater Image Enhancement

“…Physics-based methods in underwater image enhancement aim to mathematically model the image degradation process and estimate parameters to invert and obtain clear images. Existing models include scattering models [6], color balance models [7], degradation models [8], etc. For instance, Chen et al [9] employ a dark channel prior (DCP) algorithm for superpixel processing.…”