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Robust Speckle Noise Reduction in IVUS Imaging: Advancing Autoencoders and Non-Local Means with Particle Swarm Optimization

Authors :

Shirin Ashtari Tondashti¹ Navid Adib² Mehran Alyali³ Mahdis Yaghoubi⁴ Seyed Kamaledin Setarehdan⁵

1- دانشگاه تهران 2- دانشگاه تهران 3- دانشگاه تهران 4- دانشگاه تهران 5- دانشگاه تهران

Keywords :

: Intravascular Ultrasound (IVUS)،Speckle Noise،Convolutional Autoencoder،Non-Local Means،particle swarm optimization (PSO)،Data Augmentation

Abstract :

The presence of speckle noise in Intravascular Ultrasound (IVUS) images poses a significant challenge, necessitating early elimination of these artifacts. In this study, two novel methods were designed: a denoising convolutional autoencoder and a modified non-local means algorithm using particle swarm optimization (PSO) for automatic parameter selection. This research delved into assessing the efficacy of various denoising models, encompassing spatial domain techniques (median filter, non-local means, optimized non-local means), transform domain methods (wavelet, curvelet), and deep learning approaches (denoising autoencoder). A wide array of data augmentation techniques was employed during the training of the autoencoder model. Evaluation was conducted using two key metrics: Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). The findings indicate the robustness of the autoencoder model with the proposed augmentation strategy across different speckle noise variances, highlighting its superiority over traditional methods. Moreover, the study underscores the enhanced performance of modified non-local means with particle swarm optimization (PSO) compared to manual non-local means.