Denoising-based image reconstruction from pixels located at non-integer positions

TL;DR

This paper introduces a novel method for reconstructing images from pixels at non-integer positions using triangulation and adaptive denoising, significantly improving image quality in such scenarios.

Contribution

It presents a new triangulation-based initial estimate combined with an adaptive denoising framework for better image reconstruction from non-integer pixel locations.

Findings

Achieves up to 1.8 dB PSNR improvement over initial estimates.

Demonstrates effectiveness in reconstructing images with non-integer pixel positions.

Provides a robust approach for image processing operations like rotation.

Abstract

Digital images are commonly represented as regular 2D arrays, so pixels are organized in form of a matrix addressed by integers. However, there are many image processing operations, such as rotation or motion compensation, that produce pixels at non-integer positions. Typically, image reconstruction techniques cannot handle samples at non-integer positions. In this paper, we propose to use triangulation-based reconstruction as initial estimate that is later refined by a novel adaptive denoising framework. Simulations reveal that improvements of up to more than 1.8 dB (in terms of PSNR) are achieved with respect to the initial estimate.