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In this paper, we present a large-scale Diverse Real-world imageSuper-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquerSuper-Resolution (SR) network, exploring the utility of guiding SR model withlow-level image components. DRealSR establishes a new SR benchmark with diversereal-world degradation processes, mitigating the limitations of conventionalsimulated image degradation. In general, the targets of SR vary with imageregions with different low-level image components, e.g., smoothness preservingfor flat regions, sharpening for edges, and detail enhancing for textures.Learning an SR model with conventional pixel-wise loss usually is easilydominated by flat regions and edges, and fails to infer realistic details ofcomplex textures. We propose a Component Divide-and-Conquer (CDC) model and aGradient-Weighted (GW) loss for SR. Our CDC parses an image with threecomponents, employs three Component-Attentive Blocks (CABs) to learn attentivemasks and intermediate SR predictions with an intermediate supervision learningstrategy, and trains an SR model following a divide-and-conquer learningprinciple. Our GW loss also provides a feasible way to balance the difficultiesof image components for SR. Extensive experiments validate the superiorperformance of our CDC and the challenging aspects of our DRealSR datasetrelated to diverse real-world scenarios. Our dataset and codes are publiclyavailable athttps://github.com/xiezw5/Component-Divide-and-Conquer-for-Real-World-Image-Super-Resolution

Component Divide-and-Conquer for Real-World Image Super-Resolution