Evaluation of DWT denoise method on X-ray images acquired using flat detector

Naouel, Guezmir; Marrakchi, O; Mars, Mokhtar; Montfort, Jérôme

doi:10.1109/mecbme.2018.8402430

Cited by 3 publications

(1 citation statement)

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“…This work cannot process huge-noise-density teeth X-ray images. Naouel et al [23] suggest a noise removal and localization method using discrete wavelet transform and thresholding methods. This model extracts seven Regions of Interest from the images, which consumes more time which is a drawback of this work.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Impulse Noise using Partition-Supported Median, Interpolation and DWT in Dental X-Ray Images

Shajahan¹,

Aris²,

Usman³

et al. 2022

IJACSA

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The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images.

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Section: Introductionmentioning

confidence: 99%