caa a22 4500 605541108 CHVBK 20210128100914.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00371-014-0993-7 doi (NATIONALLICENCE)springer-10.1007/s00371-014-0993-7 An adaptive edge-preserving image denoising technique using tetrolet transforms [Elektronische Daten] [Paras Jain, Vipin Tyagi] Denoising has always been one of the standard problems in image processing. It is always desirable to preserve important features, such as edges, corners and other sharp structures, during denoising. Wavelet transforms have been widely used in edge-preserving image denoising since these provide a suitable basis for suppressing noisy signals from the image. This paper presents a novel edge-preserving image denoising technique based on tetrolet transform (a Haar-type wavelet transform) and a locally adaptive thresholding method. The noisy image is decomposed into tetrolet (wavelets) coefficients through a tetrolet transform. A locally adaptive thresholding method exploiting interscale statistical dependency and based on computation of noise level is used to threshold the tetrolet coefficients to effectively reduce noise while preserving relevant features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable especially for the natural images contaminated by Gaussian noise. Springer-Verlag Berlin Heidelberg, 2014 Wavelet nationallicence Tetrolet transform nationallicence Local adaptive denoising nationallicence Edge preservation nationallicence Jain Paras Jaypee University of Engineering and Technology, Raghogarh, 473226, Guna, MP, India aut Tyagi Vipin Jaypee University of Engineering and Technology, Raghogarh, 473226, Guna, MP, India aut The Visual Computer Springer Berlin Heidelberg 31/5(2015-05-01), 657-674 0178-2789 31:5<657 2015 31 371 https://doi.org/10.1007/s00371-014-0993-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0993-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jain Paras Jaypee University of Engineering and Technology, Raghogarh, 473226, Guna, MP, India aut NATIONALLICENCE 700 1- Tyagi Vipin Jaypee University of Engineering and Technology, Raghogarh, 473226, Guna, MP, India aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/5(2015-05-01), 657-674 0178-2789 31:5<657 2015 31 371