IDBP: Image Dehazing Using Blended Priors Including Non-Local, Local, and Global Priors

Abstract

In this letter, a robust and promising atmospheric scattering model (ASM)-based image dehazing technique called IDBP is developed, which overcomes the intrinsic limitation of available techniques based on single priors. It consists of two modules, i.e., an atmospheric light estimation (ALE) module and a multiple prior constraint (MPC) module. The ALE module is based on a new global brightening strategy of enhancing the brightness of image with minimum information loss. The MPC smartly blends the constrains of non-local prior, local prior, and global prior to shrink the solution space of haze removal, which avoids the limitation of using any single priors. Unlike previous works, IDBP does not require any training process, but is based on multiple priors and minimal information loss principle to impose the ASM, thereby making it easy to implement and ensuring its robustness. Numerous experiments reveal that the proposed IDBP outperforms the state-of-the-art alternates.