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 (PTOCT) three-dimensional image, an improved rotating kernel algorithm is used to suppress them. First, the PTOCT images arc decomposed by wavelet, and four sub-images with different frequency bands arc obtained. Then, the foreground and background of the low-frequency approximation sub-images arc 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 arc 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 3.68 dB, which can improve the quality of angiography imaging.