Internet of Medical Things: A CT Image Denoising in Tetrolet Domain

Diwakar, Manoj; Pandey, Richa; Sharma, Ritik; Saun, Sapna; Singh, Prabhishek; Pandey, Neeraj Kumar

doi:10.1109/iscon52037.2021.9702462

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…The obtained results are also compared with the schemes [7] and [8] dealing with the same database, and the evaluation criteria are PSNR and SSIM. The comparison results are listed in the following table 1: When the noise intensity is 10, compared with the previous best algorithms [8], the proposed method improves PSNR by 0.7dB and SSIM by 0.02.…”

Section: Ct Imagementioning

confidence: 99%

Medical image denoising based on 2D adaptive compact variational mode decomposition

Yang¹,

Shao²,

Yang³

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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This paper proposes a medical image denoising method based on two-dimensional adaptive compact variational mode decomposition (2D-ACVMD). First, a binary support function is introduced into 2D-VMD in order to enhance its spatial compactness. The optimal decomposition mode number K and the penalty factor α can be determined by the moving average power spectrum method and the genetic algorithm, respectively. Then, the decomposed high-frequency noise component is removed, and the other modes are reconstructed to obtain the denoised image. According to the experimental results, this method can effectively remove the noise interference, retain the original edge and detail features, and the PSNR and SSIM obtained are higher than the previous methods.

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Section: Ct Imagementioning

confidence: 99%