Research on the Image Denoising Method Based on Partial Differential Equations

Xie, Xiaolu

doi:10.1515/cait-2016-0057

Cited by 1 publication

(1 citation statement)

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“…Image denoising [41][42][43] is the process of restoration where the attempts are made to recover an image which is been corrupted by some noise. The presence of noise not only produces undesirable visual quality but also lowers the visibility of low contrast objects.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

Stoilova

Stoilov

Dimitrov

2020

Cybernetics and Information Technologies

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Due to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.

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Section: Related Workmentioning

confidence: 99%