Adaptive Gaussian notch filter for removing periodic noise from digital images. Issue 8 (14th May 2020)

• Record Type: Journal Article
• Authors: Varghese, Justin; Subhash, Saudia; Subramaniam, Kamalraj; Sridhar, Kuttaiyur Palaniswamy
• Is Part Of: IET image processing. Volume 14:Issue 8(2020)
• Page Start: 1529
• Page End: 1538
• Publication Date: 2020-05-14
• Subjects: optical filters -- Fourier transforms -- filtering theory -- notch filters -- image restoration -- image denoising; noisy peak positions -- associated noisy areas -- identified noisy peaks -- identified noisy peak areas -- notch filters -- peak signal‐to‐noise ratio -- AGNF -- periodic noise -- adaptive Gaussian notch filter -- digital images -- Fourier transform domain -- Moiré pattern noises -- uncorrupted images -- noisy functions -- easily distinguishable conjugate peaks; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5707
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Periodic noise corrupts digital images during acquisition and transmission stages by adding repetitive patterns. This study introduces a new adaptive Gaussian notch filter (AGNF) in Fourier transform domain for effectively restoring images contaminated with periodic, quasi‐periodic and Moiré pattern noises. Since periodic noises are sinusoidal functions added to the uncorrupted images, Fourier transform of images make these noisy functions to concentrate as easily distinguishable conjugate peaks in frequency domain. The proposed AGNF effectively identifies the noisy peak positions and adaptively quantifies the associated noisy areas by analysing the ratio of averages of frequencies from adaptively varying neighbourhoods. These identified noisy peaks are then diffused by Gaussian notch filter of adaptively varying sizes. The proposed filter ensures maximum diffusion of identified noisy peak areas by maintaining the minimum frequency values from the outputs of overlapping notch filters. Visual and quantitative experimental analysis of the proposed algorithm with mean absolute error, peak signal‐to‐noise ratio, mean structural similarity index measure and computation time reveals that AGNF is better in restoring images contaminated with periodic noises when compared to other methods.