Group sparse regularization based iterative incremental image deblurring

In the imaging process of the remote sensing, there was degradation phenomenon in the acquired images. This paper, we present a group sparse regularization based iterative incremental for remote image deblurring estimating a single latent sharp image given either a single or multiple blurry and/or noisy observations. Considering the sparseness and nonlocal similarity properties of image, a group sparse representation based incremental iterative method is established for blurry image restoration. The proposed sparse representation enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Comprehensive experiments demonstrate that the framework integrating the sparseness property of images significantly improves the deblurring performance.