State‐of‐art analysis of image denoising methods using convolutional neural networks

Thakur, R. S.; Yadav, Ram Narayan; Gupta, Lalita

doi:10.1049/iet-ipr.2019.0157

Cited by 65 publications

(31 citation statements)

References 55 publications

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“…In this case, this paper might help to choose a satisfactory ECG denoising method. Also, following the latest trends, convolutional neural networks, like DnCNN and PDNN, can be improvised for ECG signal denoising [159 ].…”

Section: Future Scopementioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

Self Cite

194

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Section: Future Scopementioning

confidence: 99%

Review of noise removal techniques in ECG signals

Chatterjee

Thakur

Yadav

et al. 2020

IET signal process.

Self Cite

194

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“…Because CNN can directly input the original image, it is simple and easy to use, is widely recognized by the academic community, and has been successfully used in the field of image denoising. Reference [ 25 ] comprehensively studies the most advanced image denoising methods using CNN. A denoising prior driven network (PDNN) is proposed to remove fixed-level Gaussian noise.…”

Section: Related Researchmentioning

confidence: 99%

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Yao

2020

Computational and Mathematical Methods in Medicine

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In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to 190 × 215 and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.

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“…The nature of image capture is extremely prone to additive noise, and commonly requires noise reduction. The use of of deep learning in this field is well established 26 and has been proven to outperform traditional techniques. Super resolution aims to accurately resize images, whilst also increasing high frequency detail.…”

Section: Deep Learningmentioning

confidence: 99%

“…The DnCNN architecture from Zhang et al 17 is the oldest network in this investigation. It is commonly used as a comparison for newer networks 20,26,41,42 .…”

Section: Dncnnmentioning

confidence: 99%

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

Vint

Caterina

Soraghan

et al. 2020

Artificial Intelligence and Machine Learning in Defense Applications II

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In long range imagery, the atmosphere along the line of sight can result in unwanted visual effects. Random variations in the refractive index of the air causes light to shift and distort. When captured by a camera, this randomly induced variation results in blurred and spatially distorted images. The removal of such effects is greatly desired. Many traditional methods are able to reduce the effects of turbulence within images, however they require complex optimisation procedures or have large computational complexity. The use of deep learning for image processing has now become commonplace, with neural networks being able to outperform traditional methods in many fields. This paper presents an evaluation of various deep learning architectures on the task of turbulence mitigation. The core disadvantage of deep learning is the dependence on a large quantity of relevant data. For the task of turbulence mitigation, real life data is difficult to obtain, as a clean undistorted image is not always obtainable. Turbulent images were therefore generated with the use of a turbulence simulator. This was able to accurately represent atmospheric conditions and apply the resulting spatial distortions onto clean images. This paper provides a comparison between current state of the art image reconstruction convolutional neural networks. Each network is trained on simulated turbulence data. They are then assessed on a series of test images. It is shown that the networks are unable to provide high quality output images. However, they are shown to be able to reduce the effects of spatial warping within the test images. This paper provides critical analysis into the effectiveness of the application of deep learning. It is shown that deep learning has potential in this field, and can be used to make further improvements in the future.

show abstract

State‐of‐art analysis of image denoising methods using convolutional neural networks

Cited by 65 publications

References 55 publications

Review of noise removal techniques in ECG signals

Review of noise removal techniques in ECG signals

A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

Analysis of deep learning architectures for turbulence mitigation in long-range imagery

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