A new image denoising framework using bilateral filtering based non-subsampled shearlet transform

Routray, Sidheswar; Malla, Prince Priya; Sharma, Sunil Kumar; Panda, Sampad Kumar; Palai, G.

doi:10.1016/j.ijleo.2020.164903

Cited by 34 publications

(29 citation statements)

References 23 publications

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“…The inputs, transformation, noise adding [97] encoding [98], and object recovery layers were used in the DDANet architecture. A skip connection [99,100] for passing high-frequency feature information was utilized. The attention gate (AG) [101] and dilated convolution were used to filter the features.…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

“…Reference [108] combined the bilateral filter, the hybrid optimization, and the CNN to remove noise. The bilateral filter [100,109] was used to remove noise, while the hybrid optimization used the swarm insight strategy [110] to preserve edges. Finally, a CNN classifier (with For the evaluation procedure, the peak signal to noise ratio, vector root mean square error, structural similarity index, and root mean square error was adopted [8,111].…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

See 1 more Smart Citation

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

197

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Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

197

View full text Add to dashboard Cite

show abstract

“…Shear wave transform [22] is based on the theory of synthetic wavelet. As an analysis tool of multi-scale geometry, it overcomes the original shortcomings of wavelet transform and generates shear wave functions with different characteristics through affine transform such as scaling, shearing, translation, and so on.…”

Section: Fast Finite Shear Wave Transformmentioning

confidence: 99%

Image fusion algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G

Cui

Wang

et al. 2021

EURASIP J. Adv. Signal Process.

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In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For example, the content of a single-mode medical image is very single, and the fused image contains more image information, which provides a more reliable basis for diagnosis. However, in wireless communication and medical image processing, the image fusion effect is poor and the efficiency is low. To solve this problem, an image fusion algorithm based on fast finite shear wave transform and convolutional neural network is proposed for wireless communication in this paper. This algorithm adopts the methods such as fast finite shear wave transform (FFST), reducing the dimension of the convolution layer, and the inverse process of fast finite shear wave transform. The experimental results show that the algorithm has a very good effect in both objective indicators and subjective vision, and it is also very feasible in wireless communication.

show abstract

“…Shear wave transform [22] is based on the theory of synthetic wavelet, and as an analysis tool of multi-scale geometry, it overcomes the original shortcomings of wavelet transform, and generates shear wave functions with different characteristics through affine transform such as scaling, shearing, translation and so on. When processing image or video by shear wave, it is decomposed into three steps:…”

Section: Fast Finite Shear Wave Transformmentioning

confidence: 99%

Image Fusion Algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G

Cui

Wang

et al. 2021

Preprint

View full text Add to dashboard Cite

In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For example, the content of single-mode medical image is very single, and the fused image contains more image information, which provides a more reliable basis for diagnosis. However, in wireless communication and medical image processing, the image fusion effect is poor and the efficiency is low. To solve this problem, a image fusion algorithm based on fast finite shear wave transform and convolutional neural network is proposed for wireless communication. Firstly, the source image is decomposed by fast finite shear wave transform (FFST); secondly, the convolution neural network is improved to reduce the dimension of convolution layer from two steps, and the number of iterations and the optimal weight of convolution neural network are determined by experiments; finally, the image is fused by the inverse process of fast finite shear wave transform. The experimental results show that the algorithm has a very good effect in both objective indicators and subjective vision, and it is also very feasible in wireless communication.

show abstract

A new image denoising framework using bilateral filtering based non-subsampled shearlet transform

Cited by 34 publications

References 23 publications

Methods for image denoising using convolutional neural network: a review

Methods for image denoising using convolutional neural network: a review

Image fusion algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G

Image Fusion Algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G

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