Abstract Single Image Super-Resolution (SISR) is a fundamental and important low-level computer vision (CV) task, yet its performance on real-world applications is not always satisfactory. Different from the previous SISR research, we focus on a specific but realistic SR issue: How can we obtain satisfied SR results from compressed JPG (C-JPG) images, which is a ubiquitous image format to greatly release storage space while missing fine details. the JPG SR task is deeply analyzed to discover the connotation. Then, we propose an effective two-step model structure named RGSR, involving two specifically designed components, i.e., JPG recovering and SR generation, instead of the perspective of noise elimination in traditional SR approaches. Besides, we further integrate the cycle loss to build a hybrid objective across scales for better SR generation. Experimental results on both of the standard test data sets and real images show that our approach achieves outstanding results and succeed in applying to practical C-JPG SR tasks.

中文翻译：

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RGSR：基于降噪的两步有损JPG图像超分辨率

Abstract Single Image Super-Resolution (SISR) 是一项基本且重要的低级计算机视觉 (CV) 任务，但其在实际应用中的性能并不总是令人满意。与之前的 SISR 研究不同，我们关注一个具体但现实的 SR 问题：我们如何从压缩的 JPG（C-JPG）图像中获得满意的 SR 结果，这是一种无处不在的图像格式，可以在丢失细节的同时大大释放存储空间。深入分析JPG SR任务，发现内涵。然后，我们提出了一种名为 RGSR 的有效两步模型结构，涉及两个专门设计的组件，即 JPG 恢复和 SR 生成，而不是传统 SR 方法中的噪声消除视角。此外，我们进一步整合循环损失以构建跨尺度的混合目标，以更好地生成 SR。