endoscopic imaging enhancement via anti-degraded model and L1L2 based variational retinex

In this paper, we propose a novel image enhancement algorithm via anti-degraded model and L1L2 based variational retinex (AD-L1L2VR) for non-uniform illumination endoscopic images. Firstly, haze-free endoscopic image is obtained by anti-degraded model named dark channel prior (DCP). For getting more accurate transmission map, it is refined by using a guided image filtering. Secondly, the haze-free endoscopic image is decomposed into detail and naturalness components by light filtering. Thirdly, a logarithmic Laplacian-based gamma correction (LLGC) is added to naturalness component for preventing color cast and uneven lighting. Fourthly, we assume that the error between detail component of haze-free image and the product of associated reflectance and background illumination follows Gaussian-Laplacian distribution. So, the associated reflectance component can be obtained by using proposed L1L2 based variational retinex (L1L2VR) model. Finally, the recombination of modified naturalness component and associated reflectance component become the final result. Experimental results demonstrate that the proposed algorithm reveals more details in the background regions as well as other interesting areas, and can mostly prevent the color cast. It has a better performance on increasing diagnosis and reducing misdiagnosis than other existing enhancement methods.