Research Article
Image Denoising Method Based on Curvelet Transform in Telemedicine
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@INPROCEEDINGS{10.1007/978-3-030-72795-6_54, author={Yang Yu and Dan Li and Likai Wang and Weiwei Liu and Kailiang Zhang and Yuan An}, title={Image Denoising Method Based on Curvelet Transform in Telemedicine}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part II}, proceedings_a={SIMUTOOLS PART 2}, year={2021}, month={4}, keywords={Curvelet transform Cyclic translation Image denoising Telemedicine}, doi={10.1007/978-3-030-72795-6_54} }- Yang Yu
Dan Li
Likai Wang
Weiwei Liu
Kailiang Zhang
Yuan An
Year: 2021
Image Denoising Method Based on Curvelet Transform in Telemedicine
SIMUTOOLS PART 2
Springer
DOI: 10.1007/978-3-030-72795-6_54
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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