An ultrasound image despeckling method using independent component analysis

Denoising as one of the most significant tools in ultrasound imaging was studied widely in the literature. However, most existing ultrasound image denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose two efficient ultrasound image denoising methods that can handle a noise mixture of various types. Our methods are based on SCoBeP and low-rank matrix completion as follows. In our first method, a noisy image is processed in blockwise manner and for each processed block we find candidate match pixels on other images using sparse coding and belief propagation, where in our second algorithm, we use overlapped patches to further lower the computation complexity. The blocks centered around these candidate pixels then will stack together and unreliable pixels will be removed using fast matrix completion method. We demonstrate the effectiveness of our algorithm in removing the mixed noise through the results. We also compare with other denoising technique using matrix completion. Our methods results in comparable performance with significantly lower computation complexity.

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An ultrasound image despeckling method using independent component analysis

Cited by 4 publications

References 13 publications

Musculoskeletal ultrasound image denoising using Daubechies wavelets

Musculoskeletal ultrasound image denoising using Daubechies wavelets

Speckle noise removal in ultrasound images using sparse code shrinkage

Mixture noise removal in ultrasound images using SCoBeP and low-rank matrix completion

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