Image denoising based on non-subsampled shearlet transform use non-local means and hard threshold

Jing Liu; Yuesong Li; Jianhui Ge

doi:10.1117/12.2540202

26 September 2019 Image denoising based on non-subsampled shearlet transform use non-local means and hard threshold

Jing Liu, Yuesong Li, Jianhui Ge

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117913 (2019) https://doi.org/10.1117/12.2540202
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

In this paper, we propose a new method for image denoising. The new method based on non-subsampled shearlet (NSST), non-local means (NLM) and hard threshold. The method splits a noised image into three parts: low frequency sub-band, band-pass sub-band, high frequency sub-band. NLM filter is used in low frequency sub-band and high frequency sub-band to remove noise after inverse NSST. The hard threshold is applied to inhibit the noise in the band-pass sub-band. Finally merge the images to get the denoised image. Experimental results on greyscale images indicate that the proposed approach is competitive with respect to peak signal to noise ratio and structural similarity index measure with several state-of-the-art algorithms especially at low noise levels.

Citation Download Citation

Jing Liu, Yuesong Li, and Jianhui Ge "Image denoising based on non-subsampled shearlet transform use non-local means and hard threshold", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117913 (26 September 2019); https://doi.org/10.1117/12.2540202

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