Manifold-Inspired Single Image Interpolation
Lantao Yu, Kuida Liu, Michael T. Orchard

TL;DR
This paper introduces a manifold-inspired method for single image interpolation that effectively handles aliasing by adaptive aliasing removal and progressive refinement, resulting in higher PSNR and better edge reconstruction.
Contribution
It presents a novel adaptive aliasing removal technique and a progressive manifold-based interpolation scheme for improved single image interpolation.
Findings
Achieves higher PSNR than existing methods.
Reconstructs edges with smoothness and sharpness.
Effectively handles severe aliasing in images.
Abstract
Manifold models consider natural-image patches to be on a low-dimensional manifold embedded in a high dimensional state space and each patch and its similar patches to approximately lie on a linear affine subspace. Manifold models are closely related to semi-local similarity, a well-known property of natural images, referring to that for most natural-image patches, several similar patches can be found in its spatial neighborhood. Many approaches to single image interpolation use manifold models to exploit semi-local similarity by two mutually exclusive parts: i) searching each target patch's similar patches and ii) operating on the searched similar patches, the target patch and the measured input pixels to estimate the target patch. Unfortunately, aliasing in the input image makes it challenging for both parts. A very few works explicitly deal with those challenges and only ad-hoc…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Advanced Vision and Imaging
