Underwater image enhancement through cooperative optimization of color, detail, contrast and multi-scale fusion

Due to the absorption and scattering of light in water, underwater images commonly exhibit degradation phenomena such as color cast, low visibility, and blurred details. In response to these issues, we propose a color, detail, contrast, and multi-scale fusion underwater image enhancement algorithm called CDCM. The algorithm first uses a color restoration method based on dark and bright channels to effectively correct color distortion of underwater images and restore their natural color balance. Secondly, utilizing morphological operations to enhance the contour and structural information of objects in the image so as to improve detail representation. In addition, the black eagle optimizer (BEO) is introduced and a new fitness function is designed to adaptively optimize image contrast. In the fusion stage, principal component weights are proposed and combined with other weighting strategies to achieve multi-scale image information fusion, enhancing the contrast while preserving rich textures and details. Experimental results on two real underwater image datasets UIEB and RUIE demonstrate that our method effectively reduces degradation phenomena, with image enhancement by improvements in color fidelity, contrast, and detail clarity compared to the existing methods. In terms of objective indicators, our method is also superior to other relevant methods, such as UCIQE, UIQM, AG, IE, PCQI, etc. Our work contributes to advancing underwater image processing techniques.