Multi-scale fusion framework via retinex and transmittance optimization for underwater image enhancement
Tie Li, Tianfei Zhou
Abstract
Low contrast, poor color saturation, and turbidity are common phenomena of underwater sensing scene images obtained in highly turbid oceans. To address these problems, we propose an underwater image enhancement method by combining Retinex and transmittance optimized multi-scale fusion framework. Firstly, the grayscale of R, G, and B channels are quantized to enhance the image contrast. Secondly, we utilize the Retinex color constancy to eliminate the negative effects of scene illumination and color distortion. Next, a dual transmittance underwater imaging model is built to estimate the background light, backscattering, and direct component transmittance, resulting in defogged images through an inverse solution. Finally, the three input images and corresponding weight maps are fused in a multi-scale framework to achieve high-quality, sharpened results. According to the experimental results and image quality evaluation index, the method combined multiple advantageous algorithms and improved the visual effect of images efficiently.