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

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....

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Bibliographic Details
Published in:Computer graphics forum Vol. 43; no. 7
Main Authors: Wu, Jiaqi, Guo, Jing, Jing, Rui, Zhang, Shihao, Tian, Zijian, Chen, Wei, Wang, Zehua
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.10.2024
Subjects:
Algorithms
Brightness
CCS Concepts
Coal mines
Color
Computing methodologies → Computer vision problems
Decoupling
Distortion
Generative adversarial networks
Image enhancement
Image processing
Light
Light reflection
Machine learning
Signal reflection
Underground mines
Unevenness
Unsupervised learning
Visibility
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary: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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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.15209