  Removing speckle noise from medical ultrasound images while preserving image
features without introducing artifact and distortion is a major challenge in
ultrasound image restoration. In this paper, we propose a multiframe-based
adaptive despeckling (MADS) algorithm to reconstruct a high-resolution B-mode
image from raw radio-frequency (RF) data that is based on a multiple input
single output (MISO) model. As a prior step to despeckling, the speckle pattern
in each frame is estimated using a novel multiframe-based adaptive approach for
ultrasonic speckle noise estimation (MSNE) based on a single input multiple
output (SIMO) modeling of consecutive deconvolved ultrasound image frames. The
elegance of the proposed despeckling algorithm is that it addresses the
despeckling problem by completely following the signal generation model unlike
conventional ad-hoc smoothening or filtering based approaches, and therefore,
it is likely to maximally preserve the image features. As deconvolution is a
necessary pre-processing step to despeckling, we describe here a 2-D extension
of the SIMO model-based 1-D deconvolution method. Finally, a complete framework
for the generation of high-resolution ultrasound B-mode image has been also
established in this paper. The results show 8.55-15.91 dB, 8.24-14.94 dB
improvement in terms of SNR and PSNR, respectively, for simulation data and
2.22-3.17, 13.24-32.85 improvement in terms of NIQE and BRISQUE, respectively,
for in-vivo data compared to the traditional despeckling algorithms. Visual
comparison shows superior texture, resolution, details of B-mode images offered
by our method compared to those by a commercial scanner, and hence, it may
significantly improve the diagnostic quality of ultrasound images.
