Overview of image noise reduction based on non-local mean algorithm

Liu, Baozhong; Liu, Jianbin

doi:10.1051/matecconf/201823203029

Cited by 14 publications

(5 citation statements)

References 17 publications

(17 reference statements)

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“…Figure 2(c) shows the image contrast increased by using CLAHE, and Figure 2(d) shows the image that is denoised by using NLFM. Liu and Liu [39] shows that the denoise using NLMF can not only reduce noise but also preserve the image structure. Based on the comparison of the peak signal ratio (PSNR) value for 24 training.tif image sample, the PNSR of the denoise image has increased by 0.46, which indicates that the noise in the CLAHE image has decreased (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization

Fauzi

Lubis²

2023

IJEECS

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The structure of the retinal blood vessels can be obtained by segmenting the fundus images. A fundus image can be gained through color fundus photography or fluorescein angiography (FA). The fundus image produced by the camera can cause noise which can reduce the quality of the fundus image. To reduce the noise, this research uses the non-local means filter (NLMF). For texture analysis, the study uses Gabor filters due to the frequencies of this filter as the same as the human visual system. The segmenting process of the retinal blood vessel is performed using K-means optimized by particle swarm optimization (PSO). The accuracy of 0.9525, the precision of 0.8330, the sensitivity of 0.5817, and the specificity of 0.9880 are obtained using the proposed method.

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Section: Resultsmentioning

confidence: 99%

Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization

Fauzi

Lubis²

2023

IJEECS

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“…All the users have performed some filtering steps before segmenting the images. They have used conventional filters such as anisotropic diffusion 35 , beam hardening correction, median filter 36 , non-local means filter 37 and ring artifacts removal; and segmentation methods such as multi-thresholding and marker-based watershed. They used implementations from open source and commercial packages such as: Avizo (TFS), Pergeos (TFS), ImageJ (open) and Mango (ANU).…”

Section: Resultsmentioning

confidence: 99%

Automatic segmentation tool for 3D digital rocks by deep learning

Phan

Ruspini

Lindseth

2021

Sci Rep

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Obtaining an accurate segmentation of images obtained by computed microtomography (micro-CT) techniques is a non-trivial process due to the wide range of noise types and artifacts present in these images. Current methodologies are often time-consuming, sensitive to noise and artifacts, and require skilled people to give accurate results. Motivated by the rapid advancement of deep learning-based segmentation techniques in recent years, we have developed a tool that aims to fully automate the segmentation process in one step, without the need for any extra image processing steps such as noise filtering or artifact removal. To get a general model, we train our network using a dataset made of high-quality three-dimensional micro-CT images from different scanners, rock types, and resolutions. In addition, we use a domain-specific augmented training pipeline with various types of noise, synthetic artifacts, and image transformation/distortion. For validation, we use a synthetic dataset to measure accuracy and analyze noise/artifact sensitivity. The results show a robust and accurate segmentation performance for the most common types of noises present in real micro-CT images. We also compared the segmentation of our method and five expert users, using commercial and open software packages on real rock images. We found that most of the current tools fail to reduce the impact of local and global noises and artifacts. We quantified the variation on human-assisted segmentation results in terms of physical properties and observed a large variation. In comparison, the new method is more robust to local noises and artifacts, outperforming the human segmentation and giving consistent results. Finally, we compared the porosity of our model segmented images with experimental porosity measured in the laboratory for ten different untrained samples, finding very encouraging results.

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“…So, we take a pixel and a small window, scan the image for similar windows, average all the windows, and substitute the normal pixel with the average. Although it consumes more time than other blurring techniques, its results are very promising as verified through manual inspection of image samples [49]. This non-local means filter is characterized by the following function [11]:…”

Section: Image Preprocessingmentioning

confidence: 97%

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Singh

Pandey

Babu

2021

Neural Comput & Applic

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COVID-19 has emerged as a global crisis with unprecedented socioeconomic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-ofthe-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with goldstandard clinical and laboratory testing.

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Overview of image noise reduction based on non-local mean algorithm

Cited by 14 publications

References 17 publications

Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization

Optimization of retinal blood vessel segmentation based on Gabor filters and particle swarm optimization

Automatic segmentation tool for 3D digital rocks by deep learning

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

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