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

• URL: http://arxiv.org/abs/2008.00195v3
• Date: Mon, 14 Sep 2020 09:22:34 GMT
• Title: Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation
• Authors: Guanghao Yin, Shouqian Sun, Chao Li, Xin Min
• Abstract summary: In real-world single image super-resolution (SISR) task, the low-resolution image suffers more complicated degradations. In this paper, we focus on the camera-screen degradation and build a real-world dataset (Cam-ScreenSR) We propose a joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is trained to model the degradation and produces more various of LR images. Then the dual residual channel attention network (DuRCAN) learns to recover the SR image.
• Score: 6.14297871633911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world single image super-resolution (SISR) task, the low-resolution image suffers more complicated degradations, not only downsampled by unknown kernels. However, existing SISR methods are generally studied with the synthetic low-resolution generation such as bicubic interpolation (BI), which greatly limits their performance. Recently, some researchers investigate real-world SISR from the perspective of the camera and smartphone. However, except the acquisition equipment, the display device also involves more complicated degradations. In this paper, we focus on the camera-screen degradation and build a real-world dataset (Cam-ScreenSR), where HR images are original ground truths from the previous DIV2K dataset and corresponding LR images are camera-captured versions of HRs displayed on the screen. We conduct extensive experiments to demonstrate that involving more real degradations is positive to improve the generalization of SISR models. Moreover, we propose a joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is trained to model the degradation and produces more various of LR images, which is validated to be efficient for data augmentation. Then the dual residual channel attention network (DuRCAN) learns to recover the SR image. The weighted combination of L1 loss and proposed Laplacian loss are applied to sharpen the high-frequency edges. Extensive experimental results in both typical synthetic and complicated real-world degradations validate the proposed method outperforms than existing SOTA models with less parameters, faster speed and better visual results. Moreover, in real captured photographs, our model also delivers best visual quality with sharper edge, less artifacts, especially appropriate color enhancement, which has not been accomplished by previous methods.

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