Restoration of dental radiographs corrupted by quantum noise via a noise-adaptive sub-global weighted sum of radiometrically similar pixels
by Manoj T. Joy; B. Priestly Shan; Geevarghese Titus
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 15, No. 3, 2022

Abstract: The image creation principle causes quantum noise in dental X-rays to interfere with image processing like registration and segmentation. The detector produces a blurry image without an object. Dental X-rays are dangerous. These factors slow image registration and segmentation. Adaptive total variation regularisation reduces noise. This study presents a customised method for suppressing quantum noise in dental X-rays. In the noise-adaptive sub-global weighted sum of radiometrically similar pixels (NSWSRSP) technique, the denoised pixel value is a weighted sum of radiometrically comparable pixels in a distorted image search-block. Noise reduction is adjustable since the smoothing parameter is proportional to the noise standard deviation in a homogeneous region. The peak-signal-to-noise ratio (PSNR) in dB for total variation (TV), directional weighted median (DWM), bilateral, anisotropic diffusion (AD), wavelet thresholding (WT), block matching (BM), and the proposed technique are 30.7619 ± 1.1744, 22.0260 ± 2.9462, 28.9104 ± 4.0115, 30.4842 ± 4.8967, 33.6781 ± 1.5863, 23.6935 ± 2.5358, and 33.8648 ± 1.6825 respectively. NSWSRSP can extract noise-free dental radiographs from quantum-noise-corrupted observations using the described technique.

Online publication date: Thu, 12-Jan-2023

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