Wavelet denoising of multiframe optical coherence tomography data using similarity measures

• Published in: IET image processing 2017-01, Vol.11 (1), p.64-79
• Main Authors: Habib, Wajiha, Sarwar, Tabinda, Siddiqui, Adil Masood, Touqir, Imran
• Format: Article
• Language: English
• Subjects: correlation methods; image degradation; image denoising; Image quality; image quality measure; multiframe optical coherence tomography data; Noise reduction; Optical Coherence Tomography; optical tomography; optimisation; optimisation criterion; Optimization; Pearson correlation coefficient; Quantitative analysis; Research Article; Similarity; similarity measures; speckle; speckle noise; SSIM; stationary wavelet transform; structural contents; structural similarity index; Wavelet transforms; wavelet‐based denoising
• ISSN: 1751-9659; 1751-9667; 1751-9667
• DOI: 10.1049/iet-ipr.2016.0160

Speckle noise is the main cause of image degradation in optical coherence tomography, which makes denoising an essential process to obtain quality images. This study proposes a wavelet-based denoising technique in which detail coefficients are assigned weights using similarity measures of Pearson's correlation coefficient and structural similarity index (SSIM). Stationary wavelet transform is used for SSIM which is an image quality measure is used as optimisation criterion to denoise images in this study. Procedure of weight computation is discussed in detail. Average of these detailed components is used to denoise the images. Comparison of proposed technique with the existing techniques has been carried out at length. Extensive qualitative and quantitative analysis reveal that the proposed technique is efficient and performs better in terms of noise reduction while maintaining the structural contents of the image.