Medical Ultrasonography Denoising Using Sparse Coding Shrinkage

A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of the new method. Results demonstrate that compared with classical denoising algorithms, the new method has increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. Our algorithm is also proved to be effective to the medical ultrasonography.