A SURVEY OF IMAGE DENOISING FILTERS BASED ON BOUNDARY DISCRIMINATION NOISE DETECTIO
Abstract
Image denoising is an essential and complex activity that should be carried out before any other image processing because it checks for errors within the image(s) and rectifies them. There are ways to remove noise, the switching scheme is an outstanding method when equated to others, it initially segregates the noisy pixels and then filters them. Boundary Discriminative Noise Detection (BDND) is a type of algorithm that uses the switching method and is good for impulse noise detection, many works have been presented using several enhancements to detect noise from images using BDND. In this paper, we present a detailed outline of impulse noise and noise removal techniques by looking at over a decade of research conducted to establish a fundamental understanding of the Boundary discriminative noise detector algorithm used in image denoising. We analyzed 19 relevant papers through Google Scholar, focusing on three aspects: the methods for detecting noisy pixels, the type(s) of noise, and the major challenges. We found that many of the image denoising methods still use BDND and at least one algorithm is developed yearly except for 2017 to 2021, indicating the algorithm is significant in the field of image denoising. Furthermore, we wrap up the survey by highlighting some research challenges and offering a list of key recommendations to spur further research in this area.
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