GPU-based total variation image restoration using Sliding Window Gauss-Seidel algorithm

Image restoration has been a research topic deeply investigated within the last two decades. As is well-known, total variation (TV) minimization by Rudin, Osher, and Fatami offers superior image restoration quality and involves solving a second order nonlinear partial differential equation (PDE). In more recent years, some effort has been made in improving computational speed for solving the associated PDE remained a bottleneck, preventing its applications to high-resolution digital images. In this paper, we improve a novel parallel algorithm Gauss-Seidel on GPU, called QL-SWGS. The algorithm is improved from the original Sliding Window Gauss Seidel proposed. As expected, our numerical results on realistic and synthetic images not only confirm that the proposed algorithm on GPU delivers quality results but also that it is many orders of magnitude faster than those algorithms on multicore CPU, particularly by at most 80% from our benchmark.