Laser & Optoelectronics Progress, Volume. 57, Issue 22, 221005(2020)
Rotating Kernel Transformation Denoising Algorithm Based on Wavelet Transform in Photothermal Optical Coherence Tomography
In view of the different types of speckle noise in the photothermal optical coherence tomography (PT-OCT) three-dimensional image, an improved rotating kernel algorithm is used to suppress them. First, the PT-OCT images are decomposed by wavelet, and four sub-images with different frequency bands are obtained. Then, the foreground and background of the low-frequency approximation sub-images are separated by the maximum between-class variance algorithm, and the segmented enhancement is performed. The improved RKT algorithm is used to filter the high frequency detailed images in horizontal, vertical and diagonal directions respectively. Finally, the low frequency approximate image and the high frequency detail image after three rotating core filtering are linearly enhanced, and then reconstructed to obtain the de-noised image. The proposed algorithm can effectively reduce the speckle noise between vessels in PT-OCT images for angiographic cross section images of brain and other complex tissues and section tomography images at different depths. Compared with the classical RKT algorithm, the square-root mean error is reduced by 27.16 on average, and the average peak signal-to-noise ratio is increased by 3.68dB, which can improve the quality of angiography imaging.
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Weiyuan Huang, Jiayi Wu, Hanhong Ren, Nanshou Wu, Bo Wei, Zhilie Tang. Rotating Kernel Transformation Denoising Algorithm Based on Wavelet Transform in Photothermal Optical Coherence Tomography[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221005
Category: Image Processing
Received: Feb. 3, 2020
Accepted: Mar. 30, 2020
Published Online: Nov. 3, 2020
The Author Email: Tang Zhilie (tangzhl@scun.cdu.com)