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Removing speckle noise from medical ultrasound images while preserving imagefeatures without introducing artifact and distortion is a major challenge inultrasound image restoration. In this paper, we propose a multiframe-basedadaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-modeimage from raw radio-frequency (RF) data that is based on a multiple inputsingle output (MISO) model. As a prior step to despeckling, the speckle patternin each frame is estimated using a novel multiframe-based adaptive approach forultrasonic speckle noise estimation (MSNE) based on a single input multipleoutput (SIMO) modeling of consecutive deconvolved ultrasound image frames. Theelegance of the proposed despeckling algorithm is that it addresses thedespeckling problem by completely following the signal generation model unlikeconventional ad-hoc smoothening or filtering based approaches, and therefore,it is likely to maximally preserve the image features. As deconvolution is anecessary pre-processing step to despeckling, we describe here a 2-D extensionof the SIMO model-based 1-D deconvolution method. Finally, a complete frameworkfor the generation of high-resolution ultrasound B-mode image has been alsoestablished in this paper. The results show 8.55-15.91 dB, 8.24-14.94 dBimprovement in terms of SNR and PSNR, respectively, for simulation data and2.22-3.17, 13.24-32.85 improvement in terms of NIQE and BRISQUE, respectively,for in-vivo data compared to the traditional despeckling algorithms. Visualcomparison shows superior texture, resolution, details of B-mode images offeredby our method compared to those by a commercial scanner, and hence, it maysignificantly improve the diagnostic quality of ultrasound images.

Multiframe-based Adaptive Despeckling Algorithm for Ultrasound B-mode Imaging with Superior Edge and Texture