Automated spectral domain approach of quasi‐periodic denoising in natural images using notch filtration with exact noise profile. Issue 7 (1st July 2018)

• Record Type: Journal Article
• Authors: Chakraborty, Debolina; Chakraborty, Anirban; Banerjee, Ayan; Bhadra Chaudhuri, Sekhar R.
• Is Part Of: IET image processing. Volume 12:Issue 7(2018)
• Page Start: 1150
• Page End: 1163
• Publication Date: 2018-07-01
• Subjects: image denoising -- image segmentation -- Fourier transforms -- image restoration -- notch filters; automated spectral domain approach -- quasi‐periodic denoising -- natural images -- notch filtration -- exact noise profile -- image processing -- elegant adaptive sinc restoration filter -- Fourier transformed corrupted image spectrum; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0307
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The domain of noise fading from digital images, by virtue of its enormous appellation amongst the researchers, stands out uniquely in the recent research field of image processing over the last few decades. Periodic noises are unintended spurious signals which often agitate an image during acquisition/transmission, thereby resulting in repetitive patterns having spatial dependency and extensively demeaning visual excellence of the image. However, high amplitude noisy spectral components are clearly noticeable from the remaining uncorrupted ones in the corresponding Fourier transformed corrupted image spectrum. Hence, it is easier to distinguish and minimise those noisy components using an appropriate thresholding and filtration technique. Therefore, to start with, a simple yet elegant model of the noise‐free natural image has been developed from the corrupted one followed by a proper thresholding method to get the noisy bitmap. Finally, an elegant adaptive sinc restoration filter with the concept of extracting the exact shape of a noise spectrum profile has been applied in the filtration phase. The performance of the proposed algorithm has been assessed both visually and statistically with other state‐of‐the‐art algorithms in the literature in terms of various performance measurement attributes, providing evidence of achieving more effective restoration with considerable lower computational time.