Medical ultrasound images are usually corrupted by the noise during their acquisition known as speckle. Speckle noiseremoval is a key stage in medical ultrasound image processing. Due to the ill-posed feature of image denoising, manyregularization methods have been proved effective. This paper introduces an approach which collaborate both sparsedictionary learning and regularization method to remove the speckle noise. The method trains a redundant dictionary byan efficient dictionary learning algorithm, and then uses it in an image prior regularization model to obtain the recoveredimage. Experimental results demonstrate that the proposed model has enhanced performance both in despeckling andtexture-preserving of medical ultrasound images compared to some popular methods.
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