Image denoising using nonsubsampled shearlet transform and twin support vector machines

Yang, Heng; Wang, Xiangyang; Niu, Panpan; Liu, Yang-Cheng

doi:10.1016/j.neunet.2014.06.007

Cited by 82 publications

(26 citation statements)

References 41 publications

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“…The effectiveness of TWSVM over other existing classification approaches has been validated on various benchmark datasets. TWSVM has better generalization ability and faster computational speed due to which it has been applied to several real life applications such as intrusion detection [60,61], activity recognition [62], image denoising [63], emotion recognition [64], text classification [65], defect prediction [66,67], disease diagnosis [68,69], and speaker identification [70]. Consider a binary classification problem of " " size.…”

Section: Twin Support Vector Machinementioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

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Section: Twin Support Vector Machinementioning

confidence: 99%

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Tomar

Agarwal

2016

Advances in Artificial Intelligence

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“…At present, there are some research results in the field of image processing such as image denoising (Qi ; Yang et al . ; Shahdoosti and Khayat ), image fusion (Kong and Liu ), and target edge detection (Yi et al . ) by NSST.…”

Section: Introductionmentioning

confidence: 99%

“…NSST can satisfy the requirement of the translation invariance. At present, there are some research results in the field of image processing such as image denoising (Qi 2013;Yang et al 2014;Shahdoosti and Khayat 2016), image fusion (Kong and Liu 2013), and target edge detection (Yi et al 2008) by NSST. The research results show that the NSST can preserve the image information, remove the random noise, and improve the SNR effectively.…”

Section: Introductionmentioning

confidence: 99%

Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

Liang

Zhang

2018

Geophysical Prospecting

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The existence of strong random noise in surface microseismic data may decrease the utility of these data. Non‐subsampled shearlet transform can effectively suppress noise by properly setting a threshold to the non‐subsampled shearlet transform coefficients. However, when the signal‐to‐noise ratio of data is low, the coefficients related to the noise are very close to the coefficients associated with signals in the non‐subsampled shearlet transform domain that the coefficients related to the noise will be retained and be treated as signals. Therefore, we need to minimise the overlapping coefficients before thresholding. In this paper, a singular value decomposition algorithm is introduced to the non‐subsampled shearlet transform coefficients, and low‐rank approximation reconstructs each non‐subsampled shearlet transform coefficient matrix in the singular value decomposition domain. The non‐subsampled shearlet transform coefficients of signals have bigger singular values than those of the random noise, which implies that the non‐subsampled shearlet transform coefficients can be well estimated by taking only a few largest singular values. Therefore, those properties of singular value decomposition may significantly help minimise overlapping of noise and signals coefficients in the non‐subsampled shearlet transform domain. Finally, the denoised microseismic data are obtained easily by giving a simple threshold to the reconstructed coefficient matrix. The performance of the proposed method is evaluated on both synthetic and field microseismic data. The experimental results illustrate that the proposed method can eliminate random noise and preserve signals of interest more effectively.

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“…The benefit of multithresholding method is presented in Binh and Khare (2010) and Chen and Han (2005). Further improvement in perceptual quality of an image can also be achieved by proper shrinkages using an optimum threshold value determined in sub-band adaptive method, which are based on either wavelet transform or wavelet packets or adaptive thresholding function (Bhutada, Anand, & Saxena, 2011b;Soni, Bhandari, Kumar, & Singh, 2013;Nasri & Nezamabadi-Pour, 2009;Yang, Wang, Niu, & Liu, 2014;Zhang, 2001;Zhang & Desai, 1998).…”

Section: Introductionmentioning

confidence: 99%

Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold

Bhandari

Kumar

Singh

et al. 2015

Journal of Experimental & Theoretical Artificial Intelligen

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In this paper, a comparative study of different wavelet filters using improved sub-band adaptive thresholding function for denoising of satellite images, based on evolutionary algorithms, has been performed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC) and particle swarm optimisation (PSO) are used for obtaining the parameters of adaptive thresholding function required for optimum performance. The visual and quantitative results clearly show the increased efficiency and flexibility of the proposed CS algorithm based on Meyer wavelet filter over various other wavelet filters for image denoising. From the comparative study of different wavelet filters, it is found that the proposed Meyer wavelet-based CS algorithm denoising approach gives better performance in terms of signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean square error (MSE) and mean as compared to ABC-and PSO-based denoising approach.The proposed technique has been tested on several satellite images. The quantitative (EKI or EPI, mean, MSE, SNR and PSNR) and visual (denoised images) results show the superiority of the proposed technique over conventional and state-of-art image denoising techniques.

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Image denoising using nonsubsampled shearlet transform and twin support vector machines

Cited by 82 publications

References 41 publications

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

Noise suppression for microseismic data by non‐subsampled shearlet transform based on singular value decomposition

Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold

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