A computational photography algorithm for quality enhancement of single lens imaging deblurring

Modern single lens reflex lenses typically consist of up to two dozen individual optical elements, the complexity of which is necessary to compensate for geometric and chromatic aberrations. This paper adopts one single lens to capture images instead of complex lenses, and computational photography technique is employed to remove corresponding imaging artifacts. We initially estimate the space-variant point spread function of the single lens by combining l1/l2 image and sparse kernel priors. A fast non-blind deconvolution method with hyper-Laplacian prior is then performed to recover clear image. Experimental results show that the proposed method is at par with state-of-the-art non-blind deconvolution approaches, especially with regards its speed that is much faster than that of existing methods.