Single-image super-resolution using iterative Wiener filter based on nonlocal means
Author(s)
Date Issued
2015
Publisher
Elsevier
Journal
Signal Processing: Image Communication
Volume
39
Issue
Part A
Start page
26
End page
45
Abstract
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.
SFU Affiliated Publication
No
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