A Spectrum-based Image Denoising Method with Edge Feature Enhancement

TL;DR

This paper introduces a novel spectrum-based image denoising method that emphasizes edge feature enhancement, aiming to improve noise suppression and detail preservation in challenging noisy images across various applications.

Contribution

The paper proposes a new spectrum-based denoising technique that integrates edge feature enhancement to outperform existing methods in noise reduction and detail preservation.

Findings

Effective noise suppression in high-noise conditions

Enhanced edge preservation compared to traditional methods

Improved image quality in medical and satellite imaging

Abstract

Image denoising stands as a critical challenge in image processing and computer vision, aiming to restore the original image from noise-affected versions caused by various intrinsic and extrinsic factors. This process is essential for applications that rely on the high quality and clarity of visual information, such as image restoration, visual tracking, and image registration, where the original content is vital for performance. Despite the development of numerous denoising algorithms, effectively suppressing noise, particularly under poor capture conditions with high noise levels, remains a challenge. Image denoising's practical importance spans multiple domains, notably medical imaging for enhanced diagnostic precision, as well as surveillance and satellite imagery where it improves image quality and usability. Techniques like the Fourier transform, which excels in noise reduction and edge preservation, along with phase congruency-based methods, offer promising results for enhancing noisy and low-contrast images common in modern imaging scenarios.