Dynamic restoration of periodic noisy image using: Upper-half spectrum

Hamd, Muthana H.; Rasool, Rabab A.

doi:10.1109/iccsii.2016.7462407

Cited by 2 publications

(1 citation statement)

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“…This noise has uncommon qualities corresponding spatially reliant besides sinusoidal in environment at products of a particular frequency. Periodic noise can be advantageously evacuated by utilizing a notch filter or narrow band reject filter [5], Uniform noise presence is essential in amplitude quantization procedure. It introduces, because of analog data changed over into digital data.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications

Bharati

Khan

Podder

et al. 2020

Studies in Systems, Decision and Control

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Noise reduction in medical images is a perplexing undertaking for the researchers in digital image processing. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as gathering of data and existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Medical images are debased through noise through its transmission and procurement. Image with noise reduce the image contrast and resolution, thereby decreasing the diagnostic values of the medical image. This paper mainly focuses on Gaussian noise, Pepper noise, Uniform noise, Salt and Speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as reorganization of images. Here four types of noises have been undertaken and applied on medical images. Besides numerous filtering methods like Gaussian, median, mean and Weiner applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), Average difference value (AD) and Maximum difference value (MD) to diminish the noises without corrupting the medical image data.

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Section: Introductionmentioning

confidence: 99%