Adaptive variational decomposition for water-related optical image enhancement

Underwater images suffer from blurred details and color distortion due to light attenuation from scattering and absorption. Current underwater image enhancement (UIE) methods overlook the effects of forward scattering, leading to difficulties in addressing low contrast and blurriness. To address the challenges caused by forward and backward scattering, we propose a novel variational-based adaptive method for removing scattering components. Our method addresses both forward and backward scattering and effectively removes interference from suspended particles, significantly enhancing image clarity and contrast for underwater applications. Specifically, our method employs a backward scattering pre-processing method to correct erroneous pixel interferences and histogram equalization to remove color bias, improving image contrast. The backward scattering noise removal method in the variational model uses horizontal and vertical gradients as constraints to remove backward scattering noise. However, it can remove a small portion of forward scattering components caused by light deviation. We develop an adaptive method using the Manhattan Distance to completely remove forward scattering. Our approach integrates prior knowledge to construct penalty terms and uses a fast solver to achieve strong decoupling of incident light and reflectance. We effectively enhance image contrast and color correction by combining variational methods with histogram equalization. Our method outperforms state-of-the-art methods on the UIEB dataset, achieving UCIQE and URanker scores of 0.636 and 2.411, respectively.