Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlocal means-based speckle filtering for ultrasound images
IEEE Transactions on Image Processing
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Speckle reducing anisotropic diffusion
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion
IEEE Transactions on Image Processing
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Ultrasound imaging is one of the most important instruments of modern medical imaging modalities. However, the usefulness of ultrasound imaging is degraded due to the presence of a signal-dependent noise known as speckle. Recently, lots of algorithms have been proposed for despeckling. Unfortunately, few attentions have been paid on time series of ultrasound images like echocardiography. In this paper, we address this problem by developing a time series non-local means (NLM) filter algorithm. A distance measure relevant to a speckle model is firstly introduced to take place of the Gaussian-weighted Euclidian distance according to a Bayesian formulation. By taking the information along the temporal axis into account, we further extend the NLM filter from single frame to image time series. To lighten the computational burden, a blockwise approach and a pre-classification process are used to accelerate the algorithm. In order to evaluate our method, experiments are conducted on both synthetic and in vivo ultrasound images. Experiments show that the proposed method achieves satisfactory results in terms of removing the speckle and preserving the edges and image details, compared with the state-of-the-art methods.