Analyzing Noise Models and Advanced Filtering Algorithms for Image Enhancement

TL;DR

This paper evaluates various filtering algorithms for image noise reduction, comparing their effectiveness across different noise types using PSNR, to identify optimal strategies for image enhancement.

Contribution

It provides a comprehensive comparison of multiple filtering techniques on various noise models, guiding the selection of appropriate filters for specific noise conditions.

Findings

Median and bilateral filters perform best on salt-and-pepper noise.

Gaussian filter is most effective for Gaussian noise.

Wiener filter shows strong performance across multiple noise types.

Abstract

Noise, an unwanted component in an image, can be the reason for the degradation of Image at the time of transmission or capturing. Noise reduction from images is still a challenging task. Digital Image Processing is a component of Digital signal processing. A wide variety of algorithms can be used in image processing to apply to an image or an input dataset and obtain important outcomes. In image processing research, removing noise from images before further analysis is essential. Post-noise removal of images improves clarity, enabling better interpretation and analysis across medical imaging, satellite imagery, and radar applications. While numerous algorithms exist, each comes with its own assumptions, strengths, and limitations. The paper aims to evaluate the effectiveness of different filtering techniques on images with eight types of noise. It evaluates methodologies like Wiener, Median, Gaussian, Mean, Low pass, High pass, Laplacian and bilateral filtering, using the performance metric Peak signal to noise ratio. It shows us the impact of different filters on noise models by applying a variety of filters to various kinds of noise. Additionally, it also assists us in determining which filtering strategy is most appropriate for a certain noise model based on the circumstances.