Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper finds that in compressed images, significantly defocused regions have better compression quality, and two regions with different defocus values possess diverse texture patterns. These observations motivate our defocus-aware quality enhancement (DAQE) approach. Specifically, we propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the regionwise defocus difference of compressed images in two aspects. (1) The DAQE approach employs fewer computational resources to enhance the quality of significantly defocused regions and more resources to enhance the quality of other regions; (2) The DAQE approach learns to separately enhance diverse texture patterns for regions with different defocus values, such that texture-specific enhancement can be achieved. Extensive experiments validate the superiority of our DAQE approach over state-of-the-art approaches in terms of quality enhancement and resource savings.

翻译：图像散焦是透镜光学像差导致的图像形成物理过程中的固有现象，能够提供丰富的图像质量信息。然而，现有压缩图像质量增强方法忽略了散焦的固有特性，导致性能欠佳。本文发现，在压缩图像中，显著散焦区域具有更好的压缩质量，且不同散焦值的区域呈现不同的纹理模式。这些观察结果启发我们提出散焦感知质量增强（DAQE）方法。具体而言，我们提出一种新颖的基于动态区域划分的深度学习架构实现DAQE方法，该方法从两方面考虑压缩图像的区域散焦差异：（1）DAQE方法投入较少计算资源增强显著散焦区域的质量，而将更多资源用于增强其他区域；（2）DAQE方法学习对具有不同散焦值的区域分别增强其多样化纹理模式，从而实现纹理特异性增强。大量实验验证了本方法在质量增强和资源节省方面均优于现有最先进方法。