POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter

TL;DR

This paper introduces an adaptive regularization method for POCS-based super-resolution image reconstruction that accounts for noise variance, improving image quality in forensic and low-resolution imaging scenarios.

Contribution

It proposes a novel adaptive regularization parameter that varies linearly with noise variance, enhancing super-resolution reconstruction performance over existing methods.

Findings

Outperforms existing super-resolution methods in tests

Effective in noisy and low-resolution conditions

Improves detail recovery in high-resolution images

Abstract

Crucial information barely visible to the human eye is often embedded in a series of low-resolution images taken of the same scene. Super-resolution enables the extraction of this information by reconstructing a single image, at a high resolution than is present in any of the individual images. This is particularly useful in forensic imaging, where the extraction of minute details in an image can help to solve a crime. Super-resolution image restoration has been one of the most important research areas in recent years which goals to obtain a high resolution (HR) image from several low resolutions (LR) blurred, noisy, under sampled and displaced images. Relation of the HR image and LR images can be modeled by a linear system using a transformation matrix and additive noise. However, a unique solution may not be available because of the singularity of transformation matrix. To overcome this problem, POCS method has been used. However, their performance is not good because the effect of noise energy has been ignored. In this paper, we propose an adaptive regularization approach based on the fact that the regularization parameter should be a linear function of noise variance. The performance of the proposed approach has been tested on several images and the obtained results demonstrate the superiority of our approach compared with existing methods.