Curvelet‐based multiscale denoising using non‐local means & guided image filter. Issue 6 (1st June 2018)

• Record Type: Journal Article
• Authors: Panigrahi, Susant Kumar; Gupta, Supratim; Sahu, Prasanna K.
• Is Part Of: IET image processing. Volume 12:Issue 6(2018)
• Page Start: 909
• Page End: 918
• Publication Date: 2018-06-01
• Subjects: image denoising -- image segmentation -- filtering theory -- image reconstruction -- approximation theory -- curvelet transforms; curvelet‐based multiscale denoising -- multiscale nonlocal means filtering -- multiscale NLM filtering -- guided image filter -- image denoising technique -- hard thresholding -- ringing artefacts -- local structure preservation -- curvelet scales -- approximation subband -- low‐frequency noise -- fine scale -- small textural details -- coarser scale -- noise subspace -- peak signal‐to‐noise ratio -- structural similarity index measure -- noise strength; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0825
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study presents an image denoising technique using multiscale non‐local means (NLM) filtering combined with hard thresholding in curvelet domain. The inevitable ringing artefacts in the reconstructed image – due to thresholding – is further processed using a guided image filter for better preservation of local structures like edges, textures and small details. The authors decomposed the image into three different curvelet scales including the approximation and the fine scale. The low‐frequency noise in the approximation sub‐band and the edges with small textural details in the fine scale are processed independently using a multiscale NLM filter. On the other hand, the hard thresholding in the remaining coarser scale is applied to separate the signal and the noise subspace. Experimental results on both greyscale and colour images indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state‐of‐the‐art algorithms.