Additive White Gaussian Noise Level Estimation for Natural Images Using Linear Scale-Space Features

Kokil, Priyanka; Pratap, Turimerla

doi:10.1007/s00034-020-01475-x

Cited by 20 publications

(6 citation statements)

References 63 publications

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“…In general, CNN is trained and tested on high quality image datasets, but in practice, it cannot be assumed that the input images are all high quality 14 . This is because in practical environments, image noise is inevitable due to the various processes involved in image acquisition, conversion and transmission 15 . Image noise is a number of isolated, randomly positioned pixels that do not reflect the true information of an image 16 .…”

Section: Introductionmentioning

confidence: 99%

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

Lü

Han

Chen

et al. 2021

Sci Rep

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A generic intelligent tomato classification system based on DenseNet-201 with transfer learning was proposed and the augmented training sets obtained by data augmentation methods were employed to train the model. The trained model achieved high classification accuracy on the images of different quality, even those containing high levels of noise. Also, the trained model could accurately and efficiently identify and classify a single tomato image with only 29 ms, indicating that the proposed model has great potential value in real-world applications. The feature visualization of the trained models shows their understanding of tomato images, i.e., the learned common and high-level features. The strongest activations of the trained models show that the correct or incorrect target recognition areas by a model during the classification process will affect its final classification accuracy. Based on this, the results obtained in this study could provide guidance and new ideas to improve the development of intelligent agriculture.

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Section: Introductionmentioning

confidence: 99%

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

Lü

Han

Chen

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…We stress that our general approach ( 15 ), largely differs from model ( 4 ) proposed in the literature, since it overcomes the problem of tuning the regularization parameter provided the noise standard deviation . In practice, it is sufficient to consider a good estimate of which can be computed by applying the efficient algorithms described in [ 19 , 24 ].…”

Section: Novel Automatically Regularized Dip-based Optimization Modelsmentioning

confidence: 99%

“…However, in real applications, choosing a reasonable value of the noise level is usually much easier than finding a suitable value of the regularization parameter . Indeed, many efficient algorithms to estimate the noise level are known in the literature [ 19 , 24 ] and successfully exploited in many fields [ 15 , 36 ]. To consider automatically regularized DIP-based optimization models is an interesting issue in the DIP framework, since so far, to the best of our knowledge, no one working in this context has been focused on this aspect.…”

Section: Introductionmentioning

confidence: 99%

Constrained and unconstrained deep image prior optimization models with automatic regularization

et al. 2022

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“…Researchers have carried out a variety of weak signal detection studies according to the needs of industries from the perspectives of the type and intensity of noise, the type of target signal, and the signal processing methods. The amplitude distribution of White Gaussian Noise (WGN) obeys Gaussian distribution, and its power spectral density obeys uniform distribution [11,12]. With components all over the spectrum, WGN is a kind of simulation noise suitable for signal-noise separation.…”

Section: Introductionmentioning

confidence: 99%

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

et al. 2022

Applied Sciences

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As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.

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Additive White Gaussian Noise Level Estimation for Natural Images Using Linear Scale-Space Features

Cited by 20 publications

References 63 publications

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

A generic intelligent tomato classification system for practical applications using DenseNet-201 with transfer learning

Constrained and unconstrained deep image prior optimization models with automatic regularization

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

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