Enhancement of underwater images based on color compensation and color line model

Aiming to address the issues of color distortion and haziness in underwater images caused by underwater light attenuation and suspended particles, a color compensation and color line model-based underwater image enhancement algorithm was proposed. Firstly, by improving the color compensation algorithm to assess and compensate for the attenuation channels in the image, the degree of color deviation was reduced and the fitting effect of the color line was improved. Secondly, by combining information entropy evaluation metrics and iterative threshold strategies, image background separation was achieved to avoid the influence of bright foreground objects, thereby enhancing the accuracy of background light estimation. Subsequently, an accelerated convex optimization algorithm was constructed using the color line and background light vectors to accurately estimate the transmittance, thus improving the image dehazing effect. Finally, normalization of the color channels could result in haze-free images without color deviation. The enhanced images could achieve an average peak signal-to-noise ratio (PSNR) of 25.36 dB, underwater color image quality evaluation (UCIQE) of 0.64, underwater image quality measure (UIQM) of 5.02, and color cast factor K of 0.02. Experimental results demonstrate that compared to other algorithms, this method can more effectively address color distortion and haziness in underwater images.