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|Title:||Single-image super-resolution using iterative Wiener filter based on nonlocal means||Author(s):||Siu, Wan Chi||Issue Date:||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.
|URI:||https://repository.cihe.edu.hk/jspui/handle/cihe/2304||DOI:||10.1016/j.image.2015.07.003||CIHE Affiliated Publication:||No|
|Appears in Collections:||CIS Publication|
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