Underwater image color restoration based on depth estimation

Underwater images are often accompanied by color casts, and color restoration of underwater images remains a challenging problem. To tackle these degradation challenges, we present a depth estimation method based on image segmentation, and completes image color restoration on this basis. Specifically, we introduce image segmentation techniques to partition the image into discrete blocks. Our findings indicate a linear correlation between the average b component value in the Lab color space for each block and the corresponding image depth. Subsequently, the segmented images are sorted by depth to facilitate the identification of image blocks that are optimal for estimating backscatter. Local lighting estimation is then performed on these blocks to calculate the image transmission map, thereby completing the color restoration of the underwater image. The color restoration achieved through this method outperforms some advanced techniques. Our image color restoration method also demonstrates good accuracy and stability across different datasets.