A Regularization by Denoising super-resolution method based on genetic algorithms.

Increasing the resolution of an image is an actual and extensively studied problem in image processing. Recently, Regularization by Denoising (RED) showing that any inverse problem can be handled by sequentially applying image denoising steps, including the image super-resolution (SR) task, which facilitate the resolution of the encountered optimization problem. In this paper, we propose a new configuration of genetic algorithms to resolve the super-resolution problem using a Non-Local Means filter as a denoiser function with a rigorous proof of the existence of a unique minimizer. In fact, since the SR algorithms always skip the complex spatial interactions within images, a more consistent model is then needed. The use of the genetic algorithms with the RED techniques guaranteed, in high intensity of noise and blur, the convergence to the globally optimal solution. As a result, the proposed algorithm shows efficient and consistent results, in terms of edges and feature preservation, compared with other SR approaches. • We treat the multi-frame super-resolution task based on genetic algorithms. • We propose the use of Nonlocal Means in the regularization term of the Regularization by Denoising cost function. • The use of genetic algorithms as an optimization approach to search the minimum of the proposed cost function gives better SR reconstruction result compared to other SR techniques. • The proposed method achieves superior computational performance in terms of PSNR and SSIM measures compared with other approaches. [ABSTRACT FROM AUTHOR]