Ultrasound Medical Image Deblurring and Denoising Method Using Variational Model on CUDA
This paper introduces a new variational model on CUDA platform for the restoration (deblurring and denoising) of ultrasound image degraded by additive Gaussian noise and blur effect. In the deblurring step, we apply an inverse algorithm with the fast transform approach. In the denoising step, a total variational model (TVM) using second-order partial anisotropic diffusion equations is used. A unique and stable solution for the proposed model is presented in terms of the Euler–Lagrange equation. Later, an accurate numerical approximation is constituted by the finite-difference-based discretization technique and the parameter dependence of the proposed model is also described. To achieve better acceleration with satisfactory performance, the proposed algorithm is properly devised on the CUDA GPU and compared with a sequential execution of the multicore CPU system. Experimental results and quantitative analysis show that our algorithm is efficient to restore the ultrasound image compared to the state-of-the-art restoration methods.
KeywordsUltrasound image denoising Partial differential equation Total variation Euler–Lagrange equation Computation Unified Device Architecture (CUDA) Graphics Processing Units (GPU) Signal-to-noise ratio
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