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In this paper, we propose a new method for impulsive noise removal from images. It uses the sparsity of natural images when they are expanded by mean of a good learned dictionary. The zeros in sparse domain give us an idea to reconstruct the pixels that are corrupted by random-value impulse noises. This idea comes from this reality that noisy image in sparse domain of original image will not have a sparse representation as much as original image sparsity. In this method we assume that the proper dictionary to achieve image in sparse domain is available  

Level-crossing (LC) analog-to-digital (A/D) converters can efficiently sample certain classes of signals. An LC A/D converter is a real-time asynchronous system, which encodes the information of an analog signal into a sequence of nonuniformly spaced time instants. In particular, this class of A/D converters uses an asynchronous data conversion approach, which is a power efficient technique. In this study, the authors propose adaptive and multi-level adaptive LC sampling models as alternatives to conventional LC schemes and apply an iterative algorithm to improve the reconstruction quality of LC A/D converters. This simulation results show that multi-level adaptive LC outperforms...