Blind deconvolution using maximum a posteriori estimates with dictionary learning

Blind deconvolution aims to obtain the original sharp image from the observed blurred image due to various distortion factors such as noise, out-of-focus, camera shake, etc. The solution to this imaging inverse problem is severely ill-posed and various heuristics in the form of some prior is required to approximate the solution. In this paper, we propose an novel deblurring algorithm using maximum a posteriori (MAP) estimation combined with sparse priors from a previously trained dictionary along with edge prior. The proposed numerical optimization methods can produce results, far better when compared to similar existing state-of-the-art methods.