A TransISP Based Image Enhancement Method for Visual Disbalance in Low-light Images

IF 2.7 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computer Graphics Forum Pub Date : 2024-10-24 DOI:10.1111/cgf.15209
Jiaqi Wu, Jing Guo, Rui Jing, Shihao Zhang, Zijian Tian, Wei Chen, Zehua Wang
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Abstract

Existing image enhancement algorithms often fail to effectively address issues of visual disbalance, such as brightness unevenness and color distortion, in low-light images. To overcome these challenges, we propose a TransISP-based image enhancement method specifically designed for low-light images. To mitigate color distortion, we design dual encoders based on decoupled representation learning, which enable complete decoupling of the reflection and illumination components, thereby preventing mutual interference during the image enhancement process. To address brightness unevenness, we introduce CNNformer, a hybrid model combining CNN and Transformer. This model efficiently captures local details and long-distance dependencies between pixels, contributing to the enhancement of brightness features across various local regions. Additionally, we integrate traditional image signal processing algorithms to achieve efficient color correction and denoising of the reflection component. Furthermore, we employ a generative adversarial network (GAN) as the overarching framework to facilitate unsupervised learning. The experimental results show that, compared with six SOTA image enhancement algorithms, our method obtains significant improvement in evaluation indexes (e.g., on LOL, PSNR: 15.59%, SSIM: 9.77%, VIF: 9.65%), and it can improve visual disbalance defects in low-light images captured from real-world coal mine underground scenarios.

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基于 TransISP 的弱光图像视觉失衡增强方法
现有的图像增强算法往往无法有效解决低照度图像中的视觉失衡问题,如亮度不均和色彩失真。为了克服这些挑战,我们提出了一种基于 TransISP 的图像增强方法,专门针对弱光图像而设计。为了减轻色彩失真,我们设计了基于解耦表示学习的双编码器,实现了反射和照明成分的完全解耦,从而防止了图像增强过程中的相互干扰。为了解决亮度不均匀问题,我们引入了 CNNformer,这是一种结合了 CNN 和 Transformer 的混合模型。该模型能有效捕捉局部细节和像素间的远距离依赖关系,有助于增强各局部区域的亮度特征。此外,我们还整合了传统的图像信号处理算法,以实现高效的色彩校正和反射成分去噪。此外,我们还采用了生成式对抗网络(GAN)作为总体框架,以促进无监督学习。实验结果表明,与六种 SOTA 图像增强算法相比,我们的方法在评价指标上获得了显著改善(例如,在 LOL 上,PSNR:15.59%,SSIM:9.77%,VIF:9.65%),并能改善真实世界煤矿井下低照度图像中的视觉失衡缺陷。
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来源期刊
Computer Graphics Forum
Computer Graphics Forum 工程技术-计算机:软件工程
CiteScore
5.80
自引率
12.00%
发文量
175
审稿时长
3-6 weeks
期刊介绍: Computer Graphics Forum is the official journal of Eurographics, published in cooperation with Wiley-Blackwell, and is a unique, international source of information for computer graphics professionals interested in graphics developments worldwide. It is now one of the leading journals for researchers, developers and users of computer graphics in both commercial and academic environments. The journal reports on the latest developments in the field throughout the world and covers all aspects of the theory, practice and application of computer graphics.
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