Underwater Image Enhancement Based on Multi-Channel Adaptive Compensation

Due to underwater light absorption and scattering, underwater images often suffer from color distortion and low contrast. However, existing underwater image enhancement methods can only solve the problem of underwater image degradation in a specific scene, and do not consider the impact of dynamic changes in water depth and water quality on underwater image degradation. In order to solve the above problems, this paper proposes a multi-channel adaptive fusion underwater image enhancement algorithm. Firstly, in order to alleviate the color distortion issue caused by light attenuation, this paper proposes a gridded adaptive channel compensation algorithm. Subsequently, the compensated image is used for multi-channel image enhancement. The first channel image is the enhanced result of the compensated image using the local entropy-constrained gray world algorithm proposed in this paper and the second channel image is the enhanced result of the first channel image using the contrast limited adaptive histogram equalization(CLAHE) algorithm. Next, the saliency weight image and brightness weight image of the two-channel images are calculated respectively. Finally, in order to combine the information advantages of different channel images, this paper adopts Laplacian-Gaussian pyramid to fuse the two-channel enhanced images and their corresponding weight images to obtain the final enhanced image. Experiments on three datasets, LSUI, UIEB and RUIE, show that the underwater images processed by the algorithm proposed in this paper have a good improvement in color and contrast, and robustness is better than the comparative methods. The UIQM value of the enhanced images is 126.844% higher than the original images, the UCIQE value is 66.370% higher than the original images, and the number of ORB feature points is 95.862% higher than the original images. Results available at https://drive.google.com/file.