A Survey on Image Denoising Techniques

Preethi, S.; Narmadha, D.

doi:10.5120/9288-3488

Cited by 16 publications

(8 citation statements)

References 5 publications

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“…e generation of noise will affect the quality of the image to a certain extent, and the visual effect presented by the image is greatly reduced [1][2][3][4]. To achieve a higher level of image processing, image denoising is particularly important [5]. In the field of traditional image denoising, there are mainly three kinds of image denoising methods, such as frequency-domain filtering and optimal linear filtering methods [6].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Cao

Jia

Zhao

et al. 2022

Mathematical Problems in Engineering

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Wavelet transformation is well applied in the field of image processing, and parameter optimization of wavelet transformation has always been an eternal topic on its performance improvement. In this paper, an adaptive self-organizing migration algorithm (ASOMA) is proposed to optimize the wavelet parameters to elevate the performance of wavelet denoising. Firstly, based on the original SOMA, an adaptive step size adjustment method is proposed by recording the step information of successful individuals, which improves the search ability of the SOMA. Secondly, an exploratory selection method of leader is proposed to effectively balance the exploration and exploitation of the SOMA. Finally, ASOMA is compared with the original SOMA and its variants using wavelet general threshold denoising on classical test images in denoising performance, which is evaluated by the indicators of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The experimental results demonstrate that ASOMA has better denoising performance than the wavelet general threshold, the original SOMA, and the related variants of SOMA.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Cao

Jia

Zhao

et al. 2022

Mathematical Problems in Engineering

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“…Inspite of colored radiological images being used in special cases, usage of grayscale version of medical image is frequently utilized in real-time environment. At present, there are various forms of pre-processing algorithms [9] as well as denoising algorithm [10], but very few of them have been tested with complicated cases of medical images.…”

Section: Introductionmentioning

confidence: 99%

Framework for comprehensive enhancement of brain tumor images with single-window operation

Murthy¹,

Sadashivappa²

2020

IJECE

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Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.

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“…More often than not, this approach will fail due to insufficient contrast between the phases, uneven illumination of the sample, surface topology of the sample, noise in the image, etc (McInerney & Terzopoulos, 1999; Despotović et al, 2010; Zhu et al, 2013). Denoising, or some other form of cleanup, will oftentimes be applied to the data in order to remove some of these artifacts before or after segmentation; however, this approach can be resource intensive, is not easily automated, and may only be partially successful (Preethi & Narmadha, 2012; Soni et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets

et al. 2019

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Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

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A Survey on Image Denoising Techniques

Cited by 16 publications

References 5 publications

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

An Adaptive Self-Organizing Migration Algorithm for Parameter Optimization of Wavelet Transformation

Framework for comprehensive enhancement of brain tumor images with single-window operation

Application of the Maximum Flow–Minimum Cut Algorithm to Segmentation and Clustering of Materials Datasets

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