Performance guarantees for the algorithms deployed to solve underdetermined linear systems with sparse solutions are based on the assumption that the involved system matrix has the form of an incoherent unit norm tight frame. Learned dictionaries, which are popular in sparse representations, often do not meet the necessary conditions for signal recovery. In compressed sensing (CS), recovery rates have been improved substantially with optimized projections; however, these techniques do not produce binary matrices, which are more suitable for hardware implementation. In this paper, we consider an underdetermined linear system with sparse solutions and propose a preconditioning technique that yields a system matrix having the properties of an incoherent unit norm tight frame. While existing work in preconditioning concerns greedy algorithms, the proposed technique is based on recent theoretical results for standard numerical solvers such as BP and OMP. Our simulations show that the proposed preconditioning improves the recovery rates both in sparse representations and CS; the results for CS are comparable to optimized projections.

Video super-resolution is a fundamental task in computer vision, aiming to enhance the resolution and visual quality of low-resolution videos. Plug-and-Play Priors is one of the most widely used frameworks for solving computational imaging problems by integrating physical and learned models. Traditional approaches often rely on handcrafted priors, which are computationally expensive and may not generalize well to diverse video content. In this paper, we propose a novel approach for video super-resolution using Plug-and-Play Priors with motion estimation. By leveraging the power of deep learning and the flexibility of the Plug-and-Play framework, our method achieves promising results while maintaining computational efficiency. Experimental results on benchmark datasets demonstrate the superiority of our approach in terms of both quantitative metrics and visual quality.

The fluctuation of quality in time is a problem that exists in motion-compensated-temporal-filtering (MCTF-) based video coding. The goal of this paper is to design a solution for overcoming the distortion fluctuation challenges faced by wavelet-based video coders. We propose a new technique for determining the number of bits to be allocated to each temporal subband in order to minimize the fluctuation in the quality of the reconstructed video. Also, the wavelet filter properties are explored to design suitable scaling coefficients with the objective of smoothening the temporal PSNR. The biorthogonal 5/3 wavelet filter is considered in this paper and experimental results are presented for 2D+t and t+2D MCTF wavelet coders.

In 3D based wavelet video coders that use longer filters, the PSNR of the reconstructed frames within a GOF varies significantly. This distortion variation is not directly related to the motion in the video sequence as in the case of hybrid video coding schemes. The paper focuses on the problem of controlling erratic variation of distortion in 3D coders. The wavelet filter properties are explored and scaling coefficients for the wavelet filter is calculated with the objective of smoothening the temporal PSNR. The biorthogonal 5/3 wavelet filter is considered in this paper and experimental results are presented for 2D+t and t+2D wavelet coders.