B-mode ultrasound is an essential part of radiological examinations due to its low cost, safety, and portability, but has the drawbacks of the speckle noise and output of most systems is two-dimensional (2D) cross sections. Image restoration techniques, using mathematical models for image degradation and noise, can be used to boost resolution (deconvolution) as well as to reduce the speckle. In this study, new single-image Bayesian restoration (BR) and multi-image super-resolution restoration (BSRR) methods are proposed for in-plane B-mode ultrasound images. The spatially correlated nature of the speckle was modeled, allowing for examination of two different models for BR and BSRR for uncorrelated Gaussian (BR-UG, BSRR-UG) and correlated Gaussian (BR-CG, BSRR-CG). The performances of these models were compared with common image restoration methods (Wiener filter, bilateral filtering, and anisotropic diffusion). Well-recognized metrics (peak signal-to-noise ratio, contrast-to-noise ratio, and normalized information density) were used for algorithm free-parameter estimation and objective evaluations. The methods were tested using superficial tissue (2D scan data collected from volunteers, tissue-mimicking resolutions, and breast phantoms). Improvement in image quality was assessed by experts using visual grading analysis. In general, BSRR-CG performed better than all other methods. A potential downside of BSRR-CG is increased computation time, which can be addressed by the use of high-performance graphics processing units (GPUs).

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A dynamically dexterous legged robot has the distinct property that the legs are continuously interacting with the environment. During walking and running, this interaction generates acoustic signals that carry considerable information about the surface being traversed, state of the robot legs and joint motors as well as the stability of the locomotion. Extracting a particular piece of information from this convolved acoustic signal however is an interesting and challenging area of research which we believe may have fundamental benefits for legged robotics research. For example, the identification of the surface that the robot travels on gives us the ability to dynamically adapt gait parameters hence improve dynamic stability. In the present paper, we investigate this particular sub-problem of surface identification using naturally occurring acoustic signals and present our results. We show that a spectral energy based feature set augmented by time derivatives and an average zero crossing rate carries enough information to accurately classify a number of commonly occurring indoor and outdoor surfaces using a popular higher dimensional vector quantizer classifier. Our experiments also suggest that VQ surface models may be velocity dependent. These initial results with a carefully collected but relatively limited dataset indicate a promising direction for our future research on improving outdoor mobility for dynamic legged robots.