Multiscale fusion of depth estimations for haze removal
Yuan-Kai Wang, Ching-Tang Fan · 2015
Restoration of haze images is important for the de-weathering issue in computer vision. The problem is ill-posed and can be regularized within a Bayesian context by using a probabilistic fusion model. This paper presents a multiscale depth fusion (MDF) method for dehazing from a single image. A linear model representing the stochastic residual of nonlinear filtering is first proposed. Multiscale filtering results are probabilistically blended into a fused depth map based on the model. The fusion is formulated as an energy minimization problem that incorporates spatial Markov dependency. An inhomogeneous Laplacian-Markov random field for the multiscale fusion regularized with smoothing and edge-preserving constraints is developed. The MDF method is experimentally verified by cluttered-depth image that is challenging for dehaze at finer details. Experimental results demonstrate that the accurate estimation of depth map by the proposed edge-preserved multiscale fusion should recover high-quality images with sharp details.