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Combining Non-Data-Adaptive Transforms for Oct Image Denoising by Iterative Basis Pursuit Publisher



Razavi R1 ; Rabbani H2 ; Plonka G1
Authors
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Authors Affiliations
  1. 1. Institute for Numerical and Applied Mathematics, Georg-August-University of Gottingen, Germany
  2. 2. Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Proceedings - International Conference on Image Processing, ICIP Published:2022


Abstract

Optical Coherence Tomography (OCT) images, as well as a majority of medical images, are imposed to speckle noise while capturing. Since the quality of these images is crucial for detecting any abnormalities, we develop an improved denoising algorithm that is particularly appropriate for OCT images. The essential idea is to combine two non-data-adaptive transform-based denoising methods that are capable to preserve different important structures appearing in OCT images while providing a very good denoising performance. Based on our numerical experiments, the most appropriate non-data-adaptive transforms for denoising and feature extraction are the Discrete Cosine Transform (DCT) (capturing local patterns) and the Dual-Tree Complex Wavelet Transform (DTCWT) (capturing piecewise smooth image features). These two transforms are combined using the Dual Basis Pursuit Denoising (DBPD) algorithm. Further improvement of the denoising procedure is achieved by total variation (TV) regularization and by employing an iterative algorithm based on DBPD. © 2022 IEEE.
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