Image Denoising Using First Order Neighborhood Mean Filter

Abstract

In digital image processing, image de-noising refers to the process of estimating a clean version of a noisy image, utilizing prior knowledge of the statistics of natural images. The challenge of evaluating such limits lies in the fact that constructing proper models of natural image statistics is a long-standing and yet unsolved problem. To address this issue, this work presents a new proposed algorithm to tackle problems, particularly poor image enhancement at high noise density, which is often encountered in the Improved Mean Filter (IMF). In this paper, Improved Mean Filtering is employed to enhance both the Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF). The performance of the proposed Improved Mean Filter (IMF) is quantitatively evaluated, and visual and human perception analyses show better results under both conditions compared to other existing filters.