Paper
10 October 2023 Low-light image enhancement combining dehazing and auxiliary channels
Yiting Wu, Chuansheng Yang, Chao Wang, Hongming Chen, Qihong Ye
Author Affiliations +
Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127995C (2023) https://doi.org/10.1117/12.3005867
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia
Abstract
Image enhancement tasks have garnered significant attention in recent years, particularly regarding images captured in low-light conditions. Due to their inherent under-illumination characteristics, these images often suffer from challenges such as low contrast, low brightness, and noise that hinder their quality. With the recent advancements in deep learning, convolutional neural networks (CNNs) have demonstrated a high potential in addressing these challenges. In this paper, we propose a deep CNN-based approach that enhances color components a and b through Lab-space decomposition of images, utilizing a deep unsupervised dehazing network model as a baseline. Our approach considers both algorithmic and image decomposition, splitting the low-light image enhancement task into two parallel subtasks. Extensive experiments demonstrate that our proposed approach outperforms previous methods.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yiting Wu, Chuansheng Yang, Chao Wang, Hongming Chen, and Qihong Ye "Low-light image enhancement combining dehazing and auxiliary channels", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127995C (10 October 2023); https://doi.org/10.1117/12.3005867
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KEYWORDS
Image enhancement

Image processing

RGB color model

Image quality

Image filtering

Light sources and illumination

Tunable filters

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