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In real-world single image super-resolution (SISR) task, the low-resolutionimage suffers more complicated degradations, not only downsampled by unknownkernels. However, existing SISR methods are generally studied with thesynthetic low-resolution generation such as bicubic interpolation (BI), whichgreatly limits their performance. Recently, some researchers investigatereal-world SISR from the perspective of the camera and smartphone. However,except the acquisition equipment, the display device also involves morecomplicated degradations. In this paper, we focus on the camera-screendegradation and build a real-world dataset (Cam-ScreenSR), where HR images areoriginal ground truths from the previous DIV2K dataset and corresponding LRimages are camera-captured versions of HRs displayed on the screen. We conductextensive experiments to demonstrate that involving more real degradations ispositive to improve the generalization of SISR models. Moreover, we propose ajoint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) istrained to model the degradation and produces more various of LR images, whichis validated to be efficient for data augmentation. Then the dual residualchannel attention network (DuRCAN) learns to recover the SR image. The weightedcombination of L1 loss and proposed Laplacian loss are applied to sharpen thehigh-frequency edges. Extensive experimental results in both typical syntheticand complicated real-world degradations validate the proposed methodoutperforms than existing SOTA models with less parameters, faster speed andbetter visual results. Moreover, in real captured photographs, our model alsodelivers best visual quality with sharper edge, less artifacts, especiallyappropriate color enhancement, which has not been accomplished by previousmethods.

Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation