Poisson Noise Reducing Bilateral Filter

Thakur, Kirti; Damodare, Omkar H.; Sapkal, A. M.

doi:10.1016/j.procs.2016.03.087

Cited by 25 publications

(8 citation statements)

References 6 publications

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“…Image preprocessing generally comprises spatial domain methods and frequency domain methods [86]. The main preprocessing algorithms include grayscale transformation, histogram equalization, various filtering algorithms based on spatial and frequency domains [99,100], etc. In addition, mathematical morphology can also be used for image denoising [101].…”

Section: Image Preprocessingmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

284

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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Section: Image Preprocessingmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

284

View full text Add to dashboard Cite

show abstract

“…This filter was developed based on the geometric and photometric distances in a local window. Kirti et al [8] modified this filter by replacing the geometric distance by Poisson distribution. Therefore, the mean value is selected as mean of image intensity in a local window.…”

Section: The Poisson Reducing Bilateral Filtermentioning

confidence: 99%

“…However, above methods are performed on iteration and this requires more execution time to remove noise. Kirti et al [8] proposed a spatial domain filter by modifying bilateral filter framework to remove Poisson noise. The Poisson reducing bilateral filter (PRBF) is non-iterative nature.…”

Section: Introductionmentioning

confidence: 99%

A Review on CT and X-Ray Images Denoising Methods

Thanh

Prasath

Hieu

2019

IJCAI

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In medical imaging systems, denoising is one of the important image processing tasks. Automatic noise removal will improve the quality of diagnosis and requires careful treatment of obtained imagery. Computed tomography (CT) and X-Ray imaging systems use the X radiation to capture images and they are usually corrupted by noise following a Poisson distribution. Due to the importance of Poisson noise removal in medical imaging, there are many state-of-the-art methods that have been studied in the image processing literature. These include methods that are based on total variation (TV) regularization, wavelets, principal component analysis, machine learning etc. In this work, we will provide a review of the following important Poisson removal methods: the method based on the modified TV model, the adaptive TV method, the adaptive non-local total variation method, the method based on the higher-order natural image prior model, the Poisson reducing bilateral filter, the PURE-LET method, and the variance stabilizing transform-based methods. Our task focuses on methodology overview, accuracy, execution time and their advantage/disadvantage assessments. The goal of this paper is to provide an apt choice of denoising method that suits to CT and X-ray images. The integration of several high-quality denoising methods in image processing software for medical imaging systems will be always excellent option and help further image analysis for computer-aided diagnosis. Povzetek: Pregledni članek opisuje metode za čiščenje slike, narejene z rentgenom ali CT.

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“…Ju Zhang et al [22] proposed a new technique by amalgamating a wavelet transform and fast bilateral filter to de-speckle the US images to detect minor tumors and helping doctors to do precise diagnosis. KirtiV.Thakur et al [23], taken advantages of the bilateral filter into consideration designed a new technique called Poison Reducing Bilateral Filter (PRBF) to remove the poison noise.…”

Section: Iiibilateralfiltermentioning

confidence: 99%

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

2019

IJITEE

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Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metrics.

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Poisson Noise Reducing Bilateral Filter

Cited by 25 publications

References 6 publications

State of the Art in Defect Detection Based on Machine Vision

State of the Art in Defect Detection Based on Machine Vision

A Review on CT and X-Ray Images Denoising Methods

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

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