Nasser Eslahi

Nasser Eslahi, Ali Aghagolzadeh, Seyed Mehdi Hosseini Andargoli

In this paper, we propose a new framework for compressive video sensing (CVS) that exploits the inherent spatial and temporal redundancies of a video sequence, effectively. The proposed method splits the video sequence into the key and non-key frames followed by dividing each frame into small non-overlapping blocks of equal sizes. At the decoder side, the key frames are reconstructed using adaptively learned sparsifying (ALS) basis via $\ell_0$ minimization, in order to exploit the spatial redundancy. Also, the effectiveness of three well-known dictionary learning algorithms is investigated in our method. For recovery of the non-key frames, a prediction of the current frame is initialized, by using the previous reconstructed frame, in order to exploit the temporal redundancy. The prediction is employed in a proper optimization problem to recover the current non-key frame. To compare our experimental results with the results of some other methods, we employ peak signal to noise ratio (PSNR) and structural similarity (SSIM) index as the quality assessor. The numerical results show the adequacy of our proposed method in CVS.

Nasser Eslahi, Hami Mahdavinataj, Ali Aghagolzadeh

The motivation of this paper is to introduce a novel framework for the restoration of images corrupted by mixed Gaussian-impulse noise. To this aim, first, an adaptive curvelet thresholding criterion is proposed which tries to adaptively remove the perturbations appeared during denoising process. Then, a new statistical regularization term, called joint adaptive statistical prior (JASP), is established which enforces both the local and nonlocal statistical consistencies, simultaneously, in a unified manner. Furthermore, a novel technique for mixed Gaussian plus impulse noise removal using JASP in a variational scheme is developed--we refer to it as De-JASP. To efficiently solve the above variational scheme, an efficient alternating minimization algorithm based on split Bregman iterative framework is developed. Extensive experimental results manifest the effectiveness of the proposed method comparing with the current state-of-the-art methods in mixed Gaussian-impulse noise removal.