改进Retinex-Net的低光照图像增强算法

doi:10.16451/j.cnki.issn1003-6059.202101008

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Abstract Aiming at the problems of high noise and color distortion in Retinex-Net algorithm, a low-light image enhancement algorithm based on improved Retinex-Net is proposed grounded on the decomposition-enhancement framework of Retinex-Net. Firstly, a decomposition network composed of shallow upper and lower sampling structure is designed to decompose the input image into reflection component and illumination component. In this process, the denoising loss is added to suppress the noise generated during the decomposition process. Secondly, the attention mechanism module and color loss are introduced into the enhancement network to enhance the brightness of the illumination component and meanwhile reduce the image color distortion. Finally, the reflection component and the enhanced illumination component are fused into the normal illumination image to output. The experimental results show that the proposed algorithm improves the image brightness effectively with the noise of enhanced image reduced.

Key words： Low-Light Image Enhancement Deep Network Retinal Cortex Theory-Net Shallow Upper and Lower Sampling Structure Attention Mechanism Module

Received: 12 May 2020

ZTFLH:

TP 391.4

Fund:National Natural Science Foundation of China(No.62076075)

Corresponding Authors: HU Xiao, Ph.D., professor. His research interests include computer vision, artificial intelligence and intelligent video analysis.

About author:: OU Jiamin, master student. Her research interests include computer vision and image processing.
YANG Jiaxin, master student. His research interests include deep learning and salient object detection.

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Cite this article:

OU Jiamin,HU Xiao,YANG Jiaxin. Low-Light Image Enhancement Algorithm Based on Improved Retinex-Net[J]. , 2021, 34(1): 77-86.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202101008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I1/77

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