结合视觉显著性与注意力机制的低光照图像增强

doi:10.16451/j.cnki.issn1003-6059.202207003

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Abstract Low-light image enhancement is the foundation and core step for solving various visual analysis tasks in low-light environments. However, the existing mainstream methods generally fail to characterize structural information effectively, resulting in some problems, such as unbalanced exposure and color distortion. Therefore, a low-light image enhancement network combining visual saliency and attention mechanism is proposed in this paper. A low-light image enhancement framework based on attention mechanism is firstly constructed by introducing attention mechanism with consideration of both local details and global information to characterize the color information in the enhancement results correctly. To achieve refined construction, a progressive process is designed to refine the enhancement process in stages following the concept of gradual optimization from coarse to fine. The feature fusion module guided by visual saliency is introduced to enhance the ability of the network to perceive salient objects in images and improve the expression of structural information from a perspective of being more in line with visual cognitive needs. Thus, noise/artifacts and other problems are avoided effectively. Experimentsshow that the proposed method solves the problems of unbalanced exposure and color distortion effectively with superior performance.

Key words： Low-Light Image Enhancement Attention Mechanism Visual Saliency Object Detection

Received: 04 January 2022

ZTFLH:

TP 311.5

Fund:Supported by Major Scientific Research Project of Zhejiang Lab(No.2019KB0AC02)

Corresponding Authors: SHANG Xiaoke, Ph.D., lecturer. Her research interests include cognitive neuroscience and artificial intelligence.

About author:: About Author:AN Nan, master student. His research interests include computer vision.SHANG Jingjie, master student. His research interests include image processing, spiking neural network application and neural network compression and deployment.ZHANG Shaomin, Ph.D., professor. His research interests include biological signal processing.DING Nai, Ph.D., professor. His research interests include cognitive neuroscience, cognitive computing and artificial intelligence.

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	SHANG Xiaoke
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Cite this article:

SHANG Xiaoke,AN Nan,SHANG Jingjie等. Combining Visual Saliency and Attention Mechanism for Low-Light Image Enhancement[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(7): 602-613.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202207003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I7/602

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