Wavelet Based Image Denoising Technique

Ruikar, Sachin D.; Doye, Dharmpal D.

doi:10.14569/ijacsa.2011.020309

Cited by 44 publications

(3 citation statements)

References 18 publications

(12 reference statements)

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“…Wavelet-based image de-noising is a multi-resolution image analysis technique that uses different mother wavelets such as Daubechies, Haar etc., to obtain wavelet coefficients. It has been used to de-noise Gaussian, salt and pepper, and Poisson noise using the appropriate thresholding operator [18], [19]. In recent years, the most promising non-local means, collaborative filtering method in the transform domain is block-matching and 3D filtering (BM3D) [20].…”

Section: B Classification Of Image De-noising Techniquesmentioning

confidence: 99%

Image De-Noising With Machine Learning: A Review

et al. 2021

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Images are susceptible to various kinds of noises, which corrupt the pictorial information stored in the images. Image de-noising has become an integral part of the image processing workflow. It is used to attenuate the noises and accentuate the specific image information stored within. Machine learning is an important tool in the image-de-noising workflow in terms of its robustness, accuracy, and time requirement. This paper explores the numerous state-of-the-art machine-learning-based image de-noisers like dictionary learning models, convolutional neural networks and generative adversarial networks for a range of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. The motivation, algorithm and framework of different machine learning de-noisers are analyzed. These de-noisers are compared using PSNR as quality assessment metric on some benchmark datasets. The best de-noising results for different noise type is discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. CNN+LSTM, and MC2RNet are most suitable CNNbased Poisson de-noisers. For impulse noise removal, Blind CNN, and CNN+PSO perform well. For mixed noise removal, WDL, EM-CNN, CNN, SDL, and Mixed CNN are prominent. De-noisers like GRDN and DDFN show accurate results in the domain of real-world de-noising.

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Section: B Classification Of Image De-noising Techniquesmentioning

confidence: 99%

Image De-Noising With Machine Learning: A Review

et al. 2021

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“…The performance of wavelets is generally scaled in the number of wavelets formed and the value of threshold selected for filtering. The shrinking methods, Visu shrink and Sure shrink by Ruikar and Doye tends to minimize median and mean square error in wavelets respectively [8]. Many schemes exist that enhance the performance of wavelets yet its performance over ICA and problems related to ICA are yet a matter of discussion.…”

Section: Introductionmentioning

confidence: 99%

Polycystic Ovarian Syndrome Detection by Using Two-Stage Image Denoising

Choubey¹,

Choubey²,

Nandan³

et al. 2021

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The requirement of imaging methods in the medical field is vivid. If the capturing devices are not sophisticated, the acquired images will have a significant amount of noise. These noises are hazardous and cannot be entertained. Polycystic Ovarian Syndrome (PCOS) caused the state of affairs in girls if not diagnosed and look after early stages. Tran's epithelial duct ultrasound machine could be a non-invasive technique of imaging the human ovary to show salient options necessary for PCOS identification. Numbers of follicles and their sizes area unit the most options that characterize ovarian pictures. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. conflict in medical aid is essentially created by technical advances in modalities that resulted from fruitful interactions among the essential science, bioscience, and manufacturer. Hence, PCOS is diagnosed by investigating the numbers of follicles and measurement their sizes manually. This paper attempts to identify the noise & try to generate a noise-free image by evaluation of noise properties. The noise pattern information thus provides an upper hand in the second stage filtering with specific filters i.e. fuzzified. A median filter for salt and pepper noise; and an adaptive wiener filter for Gaussian noise. 46.2%, 15.1%, and 12.4% improvement in MSE for salt & pepper, Gaussian, and speckle noise as compared to best existing methods.

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“…It is worth noting that, some methods such as classical wavelet transform, two-dimensional empirical mode decomposition (BEMD), and two-dimensional variational mode decomposition (2D-VMD), which are based on multi-scale image decomposition, are used for image denoising. The wavelet transform has been widely used in image denoising due to the good time-frequency characteristic (Jianhua et al, 2019;Liu, 2015;Ruikar & Doye, 2012). Nevertheless, in the wavelet filtering method, the edge of the image is seen as the highfrequency signal and smoothed out, and it also needs the selection of an appropriate wavelet basis.…”

Section: Introductionmentioning

confidence: 99%

An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image

Gao

Dong

et al. 2020

Systems Science & Control Engineering

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In this paper, an image denoising algorithm is presented based on the two-dimensional variational mode decomposition (2D-VMD) and the Hausdorff distance (HD). The procedure of the developed algorithm is that: (1) use the 2D-VMD to decompose the image into a number of intrinsic mode functions (IMFs); (2) use the HD and the probability density function (PDF) to distinguish the signaldominant IMFs (S-D IMFs) and the noise-dominant IMFs (N-D IMFs), and then use the wavelet threshold denoising method to eliminate the noise in the N-D IMFs; and (3) use the denoised IMFs and the S-D IMFs to reconstruct the image and obtain the denoised image. The effectiveness of the proposed algorithm is verified on the oil pipeline images. Simulation results show that, compared with the median filtering, the wavelet threshold filtering, and the Rudin Osher Fatemi, the proposed algorithm has a better denoising effect in both subjective and objective evaluation.

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Wavelet Based Image Denoising Technique

Cited by 44 publications

References 18 publications

Image De-Noising With Machine Learning: A Review

Image De-Noising With Machine Learning: A Review

Polycystic Ovarian Syndrome Detection by Using Two-Stage Image Denoising

An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image

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