A sparseland model for deblurring images in the presence of impulse noise
Haili Zhang, Yunmei Chen · 2012
Joint image deblurring and denoising has long been an interesting problem. Traditional deconvolution methods (like the ROF model) only work for Gaussian noise. Median-based approaches are generally concerned with the removal of impulse noise, which are more likely to hamper the deblurring process. In this paper, we propose a spareland model for deblurring images corrupted by impulse noise. The key point is to approximate the probability density function by two different randomly mixed Gaussian distributions. Experimental results are provided at the end of this paper to demonstrate the effectiveness of the proposed method.