DAQE: Enhancing the Quality of Compressed Images by Finding the Secret
of Defocus
- URL: http://arxiv.org/abs/2211.10984v1
- Date: Sun, 20 Nov 2022 14:08:47 GMT
- Title: DAQE: Enhancing the Quality of Compressed Images by Finding the Secret
of Defocus
- Authors: Qunliang Xing, Mai Xu, Xin Deng and Yichen Guo
- Abstract summary: Existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance.
We propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the region-wise defocus difference of compressed images in two aspects.
The DAQE approach learns to separately enhance diverse texture patterns for the regions with different defocus values, such that texture-wise one-on-one enhancement can be achieved.
- Score: 52.795238584413
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Image defocus is inherent in the physics of image formation caused by the
optical aberration of lenses, providing plentiful information on image quality.
Unfortunately, the existing quality enhancement approaches for compressed
images neglect the inherent characteristic of defocus, resulting in inferior
performance. This paper finds that in compressed images, the significantly
defocused regions are with better compression quality and two regions with
different defocus values possess diverse texture patterns. These findings
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 region-wise defocus difference of compressed
images in two aspects. (1) The DAQE approach employs fewer computational
resources to enhance the quality of significantly defocused regions, while more
resources on enhancing the quality of other regions; (2) The DAQE approach
learns to separately enhance diverse texture patterns for the regions with
different defocus values, such that texture-wise one-on-one enhancement can be
achieved. Extensive experiments validate the superiority of our DAQE approach
in terms of quality enhancement and resource-saving, compared with other
state-of-the-art approaches.
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