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Geometrical X-Lets for Image Denoising Publisher Pubmed



Khodabandeh Z1 ; Rabbani H2 ; Mehri A2
Authors
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Authors Affiliations
  1. 1. Stud. Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Medical Images and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Postal Code:, 81745319, Iran

Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Published:2019


Abstract

There has been a lot of researches allocated to image denoising in recent years. One of the appropriate approaches for image denoising is applying nonlinear thresholding techniques in time-frequency transform domains. These transforms decompose an image to a series of elementary waveforms called basis functions or dictionary atoms. Different directional time-frequency dictionaries provide various geometrical X-let transforms in two or higher dimensions. In this paper, we have a comparative study of geometrical X-let transforms including 2D-Discrete Wavelet (2D-DWT), Dual-Tree Complex Wavelet (DT-CWT), Curvelet, Contourlet, Steerable Pyramid (STP) and Circlet Transform (CT) in application of image denoising. Experimental results show that in synthetic images of Optical Coherence Tomography (OCT), the Steerable Pyramid outperforms other geometrical X-lets in terms of Peak Signal-to-Noise Ratio (PSNR), while DT-CWT is superior in terms of Structural Similarity Index (SSIM). Moreover, in real images of OCT which consist of retinal layers, Curvelet Transform has better results in terms of Contrast-to-Noise Ratio (CNR) and 2D-DWT is better in Edge Preservation (EP) and Texture Preservation (TP) which indicate various X-lets can be effective due to different criteria and different images. © 2019 IEEE.
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