Single image haze removal using Gaussian mixture model and sparse optimization
Single image haze removal is an underdetermined inverse problem whose solution hinges on valid image priors or models. In this work, robust priors drawn from outdoor scene statistics are explored. Specifically, a Gaussian mixture model of chrominance distribution is proposed toward transmittance estimation and its physical validity is justified. In addition, a new sparsity-based optimization approach for transmittance image super-resolution/restoration is proposed, which makes a solid assumption that most outdoor object surfaces are piece-wise linear and thus the corresponding depth image is sparse in Laplacian space. Experimental results are given in proof of the remarkably improved visual quality of our new haze removal technique over its predecessors.