Hybrid denoising technique for suppressing Gaussian noise in medical images

Chithra, K.; Santhanam, T.

doi:10.1109/icpcsi.2017.8391954

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…Filtering, on the other hand, involves the extraction of useful information from a signal or dataset while minimizing noise or unwanted artifacts. Also, in hybrid fuzzy filtering, fuzzy logic is employed to model the uncertainty and imprecision inherent in the data or signal being processed [22]. This can be particularly useful in scenarios where traditional filtering approaches may struggle due to complex or ambiguous input characteristics [23].…”

Section: Hybrid Filteringmentioning

confidence: 99%

Enhancing the Quality of 2D and 3D Image Using Block Matching 3D and Fuzzy Hybrid Model Based on Random Noise Reduction

Biswas,

Bushra,

Islam

et al. 2024

Preprint

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Medical imaging has become an important research field that embraces a great variety of sciences. Imaging is the central science of measurement in the diagnosis and treating diseases. The effort of technological progress has made possible human imaging starting from a single molecule to the whole body. A new strategy for suppressing random noise in medical images has been proposed in this research. This methodology is founded on a fuzzy filter which follows BM3D (Block Matching 3D) and fuzzy hybrid model. In this comprehensive study, we address the challenges posed by random noise in 2D and 3D images through a unique approach of image denoising. As high-quality medical imaging (Ultrasound, CT-Scan and MRI) is required to obtain a quick and accurate diagnosis. But the problem is that the image can frequently be contaminated by several types of noises including speckle noise, salt & pepper noise, and Gaussian noise. For RGB and grayscale images, some traditional fuzzy membership function is adeptly employed to eliminate these random noises. But here, our proposed research methodology also encompasses 2D and 3D techniques for medical and non-medical image denoising. This research contributes to advancing the field, offering insights into effective strategies for enhancing the quality and accuracy of images crucial for diagnostic and treatment purposes.

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Section: Hybrid Filteringmentioning

confidence: 99%

Enhancing the Quality of 2D and 3D Image Using Block Matching 3D and Fuzzy Hybrid Model Based on Random Noise Reduction

Biswas,

Bushra,

Islam

et al. 2024

Preprint

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show abstract

“…The new hybrid method showed a decrease of 73.76% MSE value and an increase of 26.24% concerning the PSNR value. The indicated outcomes that the suggested approach in their study was more-suitable for eliminating Gaussian noise in the field of medical images (CT) than other algorithms [43].…”

Section: Wavelet Domain and Filtering Denoising Techniquesmentioning

confidence: 99%

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

Ismael¹,

Baykara²

2021

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Recently, with the explosion in the number of digital images captured every day in all life aspects, there is a growing demand for more detailed and visually attractive images. However, the images taken by current sensors are inevitably degraded by noise in various fields, such as medical, astrophysics, weather forecasting, etc., which contributes to impaired visual image quality. Therefore, work is needed to reduce noise by preserving the textural, information, and structural features of the image. So far, there are different techniques for reducing noise that various researchers have done. Each technique has its advantages and disadvantages. In this paper, a review of some significant work in the field of image denoising based on that the denoising methods is presented. These methods can be classified as wavelet domain, spatial domain, or both methods can combine to obtain the advantage them. After a brief discussion, the classification of image denoising techniques is explained. A comparative analysis of various image denoising methods is also performed to help researchers in the image denoising area. Besides, standard measurement parameters have been used to compute the results and to evaluate the performance of the used denoising techniques. This review paper aims to provide functional knowledge of image denoising methods in a nutshell for applications using images to provide ease for selecting the ideal strategy according to the necessity.

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“…In 2017, Chithra and Santhanam [21] proposed a hybrid filter. It is used for suppressing the Gaussian noise in computer tomography (CT) for improving the quality of the image.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient image denoising method based on KPDE

Shroff¹,

Patidar²,

Singh³

2018

IJATEE

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In this paper a k-means based PDE has been applied for image denoising. In this approach first data pre-processing mechanism has been applied. The next procedure is for the image denoising. In this process the pre-processed image has been selected. Gaussian noise has been added in terms of noise percentage. Then object based clustering and decomposition has been applied for efficient data point selection. For this k-means algorithm has been applied. By this process object point cluster has been obtain. The main benefit by this approach is it is able in finding the decomposition as well as the similar point by the similarity ranking and matching. PDE-FFT hybridization has then been applied on the clustered data for the final noise separation. Then the PSNR values have been calculatedfor the comparative study. The results indicated that our approach has the capability in better noise removal in terms of previous method.

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Hybrid denoising technique for suppressing Gaussian noise in medical images

Cited by 12 publications

References 6 publications

Enhancing the Quality of 2D and 3D Image Using Block Matching 3D and Fuzzy Hybrid Model Based on Random Noise Reduction

Enhancing the Quality of 2D and 3D Image Using Block Matching 3D and Fuzzy Hybrid Model Based on Random Noise Reduction

Digital Image Denoising Techniques Based on Multi-Resolution Wavelet Domain with Spatial Filters: A Review

An efficient image denoising method based on KPDE

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