Application of Debauchies wavelet to the de-noising of radio maps

Chen, Puchun; Zhang, Xizhen; Shen, Xiang; Feng, Long-Long; Reich, W.

doi:10.1016/s0275-1062(01)00052-2

Cited by 1 publication

(1 citation statement)

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“…Important developments also took place in this field as parallel with the development of multi-resolution analyses. Firstly, the wavelet transform was put forth, creating the basis of multi-resolution analyses and various successful denoising applications followed through by using the wavelet transform and different algorithms [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Real and Complex Valued Ripplet-I Transform for Medical Image Denoising and Analysis of Thresholding Constants and Scales Effects

Yaşar

Ceylan

2017

JBEMI

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Medical image processing is an important diagnostic tool in the field of medical. Medical images might be affected by the noises that manipulate the resolution negatively during screening or transmission. These images need to be eliminated so as not to affect the diagnosis success negatively. In medical image denoising studies, using the multi-resolution analysis coefficients is a widely appreciated method. This study tested the success rate of real and complex valued ripplet-I transform for medical image denoising. Thanks to this study, the complex version of the newly suggested ripplet-I transform whose real version was used formerly in various studies was used in a medical image denoising application the first time. In the study tested with 40 liver images, 40 retinal images and 322 mammographic images, peak signal-to-noise ratio (PSNR), mean structural similarity index (MSSIM) and feature similarity index (FSIM) were utilized to compare the successes of image denoising. In the wake of study, it was seen that the complex valued ripplet-I (CVR-I) transform gave better results than the real valued ripplet-I (RVR-I) transform when used in the same image denoising algorithm. This study also examined the effects that the changes in scale and thresholding constant values have on the medical image denoising results, thus making this study appear as a guideline.

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