Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization

Due to the selective attenuation of light in water, captured underwater images exhibit poor visibility and cause considerable challenges for vision tasks. The structural and statistical properties of different regions of degraded underwater images are damaged at different levels, resulting in a global nonuniform drift of the feature representation, causing further degradation of visual performance. To handle these issues, we present an underwater image enhancement method via multi-interval subhistogram perspective equalization to address the issues posed by underwater images. We estimate the degree of feature drifts in each area of an image by extracting the statistical characteristics of the image, using this information to guide feature enhancement to achieve adaptive feature enhancement, thereby improving the visual effect of degraded images. We first design a variational model that uses the difference between data items and regular items to improve the color correction performance of the method based on subinterval linear transformation. In addition, a multithreshold selection method, which adaptively selects a threshold array for interval division, is developed. Ultimately, a multi-interval subhistogram equalization method, which performs histogram equalization in each subhistogram to improve the image contrast, is presented. Experiments on underwater images with various scenarios demonstrate that our method significantly outperforms many state-of-the-art methods qualitatively and quantitatively.