(1) Departamento de Matematica Aplicada, Universidad de Valencia, C/ Dr Moliner, 50, 46100 Burjassot, Spain;(2) Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90095, USA
Abstract:
In this paper we formulate a new time dependent convolutional model for super-resolution based on a constrained variational model that uses the total variation of the signal as a regularizing functional. We propose an iterative refinement procedure based on Bregman iteration to improve spatial resolution. The model uses a dataset of low resolution images and incorporates a downsampling operator to relate the high resolution scale to the low resolution one. We present an algorithm for the model and we perform a series of numerical experiments to show evidence of the good behavior of the numerical scheme and quality of the results.