弱光条件下深度视网膜图像增强算法

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC) Pub Date : 2023-02-24 DOI:10.1109/ITNEC56291.2023.10082369

Jiu-long Zhao, Zi-Yuan Chen, Hong-yue Jiang, Qian Zhang

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引用次数: 0

摘要

针对弱光条件下反射分量色差大、光照分量细节度低的问题，提出了一种改进的深度Retinex增强算法。在增强的网络中嵌入卷积块注意模块(CBAM)来提取图像的空间和通道信息，以改善图像的颜色失真。采用双线性插值方法，利用相邻空间信息的权重来突出细节。最后逐像素合并R、L分量得到增强图像。实验结果表明，该算法的主观视觉效果更加自然，客观评价指标有较大提高。

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Deep Retinex image enhancement algorithm under weak Light Conditions

An improved deep Retinex enhancement algorithm is proposed to solve the problems of large color deviation of reflection component and low detail of illumination component in low light condition. The Convolutional Block Attention Module (CBAM) is embedded in the enhanced network to extract the spatial and channel information of the image to improve the color distortion. Bilinear interpolation method was used to highlight details with the weight of adjacent spatial information. Finally, the enhanced image was obtained by merging R and L components pixel by pixel. The experimental results show that the subjective visual effect of the algorithm is more natural, and the objective evaluation indexes are greatly improved.

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来源期刊

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)

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