Abstract:

Currently, low illumination enhancement algorithms applied to crude oil storage stations are prone to color distortion and excessive sharpening in the enhanced images due to the low illumination and contrast of on-site images. Therefore a low brightness image enhancement method is proposed based on LogRetinex-Net. First, a logarithmic transformation layer is introduced to the Retinex-Net to enhance the overall grayscale of the images, reducing the impact of low illumination and contrast in crude oil storage station images. Then, a channel attention mechanism is utilized to increase the network’s focus on color channels, thereby mitigating the issue of color distortion. Finally, the model is trained and validated on a crude oil storage station dataset. Experimental results show that the LogRetinex-Net network improves artifacts and over-sharpening phenomena, significantly enhancing image quality.

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