3 accepted papers
Linear Inverse Problems (LIPs) with non-negative sparse constraints on the target signal are critical in numerous applications across various fields. Among the recently proposed methods dealing with LIPs, model-based deep learning methods, particularly deep unrolling, have gained popularity due to t…
Recovering a sparse target vector with reduced sparsity from a given observation vector is a major challenge in many applications. The well-known tail-minimization approaches tackle this challenge by minimizing the tail part of the target vector. Building upon this, recent development, the tail fast…
The non-negative least squares (NNLS) aims at finding a non-negative approximation of a matrix system. Such an approximation has been realized in the literature via some iterative methods. Recently, unrolling algorithms have gained significant attention due to their superior approximation results co…