基于卷积神经网络的逆光图像增强研究

doi:10.16088/j.issn.1001-6600.2020121505

Abstract

Abstract: Most of the existing algorithms can only enhance the backlight images with specific illumination, but cannot solve the backlight images with various illuminations efficiently. Therefore, an image enhancement algorithm based on convolutional neural network is proposed in this paper, and a new network architecture that integrates decomposition, recovery and adjustment is built at the same time. Using Retinex theory, a decomposition network is designed to decompose the backlight image and its corresponding highlight image into reflectance map and illumination map. The reflectance component of highlight image is used as the denoising reference to repair the dark light defect, and the color saturation module is added to retain the color and other details in the image restoration process. The brightness of the backlight images can be adjusted adaptively according to the user's preference. The enhancement ratio (the ratio between the target light source and the image light source) is set as the adjustment index. When the backlight images are enhanced to the high-light images, the enhancement ratio should be greater than 1. Validated on multiple public datasets (LOL, DICM, NPE), the research shows that this method can effectively enhance the brightness of backlight images, improve image quality, ensure that image details are not lost, and avoid color distortion. It has good effects on backlight images with different illuminations, and the results of subjective and objective evaluation indicators are better than the existing algorithms, which has application value for the development of smart city security and artificial intelligence.

Key words: backlight image enhancement, convolutional neural network, Retinex, color saturation, artificialintelligence

CLC Number:

TP391.41

MA Chengxu, ZENG Shangyou, ZHAO Junbo, CHEN Hongyang. Research on Backlight Image Enhancement Based on Convolutional Neural Network[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(2): 81-90.

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Research on Backlight Image Enhancement Based on Convolutional Neural Network

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