Image Denoising Method Based on Curvelet Transform in Telemedicine.
28 Aug 2020-pp 679-690
TL;DR: Simulation experiments confirm that the new method of image denoising reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.
Abstract: To resolve the problems that the traditional image denoising methods are easy to lose details such as edges and textures, a new method of image denoising was proposed. It based on the Curvelet denoising algorithm, using polynomial interpolation threshold method, combining with Wrapping and Cycle spinning techniques to determine the adaptive threshold of each Curvelet coefficient for denoising the medical images. Simulation experiments confirm that the new method reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.
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TL;DR: The proposed wrapping-based curvelet transform method for fusion of infrared and visible images retains background details as well as hot target presence with fidelity and is visually and statistically better in comparison to other fused image outputs.
Abstract: Image fusion has been widely used to combine multispectral information into an enhanced information image. The application of such enhanced information content in the field of surveillance for improving situation awareness of observer is highly recommended. When a single sensor information is used for surveillance like visible camera output during poor ambient lighting conditions, ‘hot-target’ details are not visible to the observer. The use of visible-infrared fused image is recommended during surveillance in poor ambient lighting conditions to visualise background scene details and ‘hot-target’ details simultaneously. A wrapping-based curvelet transform method is proposed for fusion of infrared and visible images. Curvelet transform is used because of its advantages over wavelet transform limitations like directional insensitivity, isotropic basis and inability to resolve curves. The approximation coefficients are fused using the principal component analysis rule while detailed coefficients are ...
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Abstract: The generalized finite-element method (GFEM) enriched by plane waves has been proved suitable to solve the 2-D Helmholtz equation. However, when the number of unknowns increases, some difficulties arise, such as bad condition number. Therefore, developing a pre-processing methodology to identify an appropriate number of degrees of freedom for each problem could be useful. This paper presents a systematic approach to determine the optimal number of plane waves for each problem to be used in GFEM solution. It is based on the analysis of the traditional FEM solution with a poor mesh resolution using the fast discrete curvelet transform. The results indicate that the adopted strategy can guarantee accurate and converging responses for GFEM in complex problems, along with remarkable reductions of the computational cost.
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