Single-image super-resolution using iterative Wiener filter based on nonlocal means

• Nonlocal means filter as the correlation function of the Wiener filter.
• Iterative scheme to resolve drawbacks of the current non-iterative approaches.
• Offline training of parameters through minimizing the MSE of the training image.
• High competitive performance compared with state-of-the-art approaches in terms of PSNR and SSIM.
• Can be applied to content specific applications, e.g. face super-resolution.

In this paper, we propose a single-frame super-resolution algorithm using a finite impulse response (FIR) Wiener-filter, where the correlation matrices are estimated using the nonlocal means filter. The major contribution of this work is to make use of the nonlocal means-based FIR Wiener filter to form a new iterative framework which alternately improves the estimated correlation and the estimated high-resolution (HR) image. To minimize the mean squared error of the estimated HR image, we have tried to optimize several parameters empirically, including the hyper-parameters of the nonlocal means filter by using an efficient offline training process. Experimental results show that the trained iterative framework performs better than the state-of-the-art algorithms using sparse representations and Gaussian process regression in terms of PSNR and SSIM values on a set of commonly used standard testing images. The proposed framework can be directly applied to other image processing tasks, such as denoising and restoration, and content-specific tasks such as face super-resolution.