Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN

Abiko, Ryo; Ikehara, Masaaki

doi:10.1109/icassp.2019.8683878

Cited by 26 publications

(24 citation statements)

References 17 publications

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“…Thus, a combination of the proposed IDCNN for impulse detection and a CNN for noisy pixel restoration will significantly increase the overall performance of noisy image enhancement. The performed experiments indicate that the BDCNN output [72] could be used for the restoration of the detected noisy pixels. Another research direction will be focused on the elaboration of a single network able to combine both the detection and noisy pixel restoration in one processing stage.…”

Section: Discussionmentioning

confidence: 96%

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Radlak

Maliński

Smołka

2020

Sensors

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Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

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Section: Discussionmentioning

confidence: 96%

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Radlak

Maliński

Smołka

2020

Sensors

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“…The squared Frobenius norm is used as the loss function, and training is done by the back propagation algorithm. The other CNN model for mixed Gaussian and impulse noise involves two parts: the first half for impulse noise removal and the second half for the Gaussian noise removal [83]. It consists of the input layer, intermediate layers of convolution, batch normalization, and leaky ReLU followed by the convolutional output layer.…”

Section: B Methodologies Of Cnn-based Models (Mixed Noise)mentioning

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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“…3), to evaluate the performance of our algorithm. We compare the proposed NLRM-PG method with several state-of-the-art methods, namely weighted encoding with sparse nonlocal regularization (WESNR) [6] method, weighted low rank model (WLRM) [14] method, Structure tensor total variation-regularized weighted nuclear norm minimization(STTV-WNNM) [17] method, laplacian scale mixture and nonlocal low-rank approximation(LSM-NLR) [18] method, weighted nuclear norm minimization(WNNM) [25] method, Nonconvex Low Rank Approximation (NLRA) [29] method, Robust PCA via nonconvex rank approximation(RPCA-NRA) [31] method and DSCNN [44] method.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…In recent years, based on deep learning methods for removing image noise were proposed [41]- [44]. Convolutional neural networks(CNN) [41] were exploited for natural image denoising.…”

Section: Introductionmentioning

confidence: 99%

“…A deep learning method having outstanding performance was found. Hybrid noise removal method with deep single CNN(DSCNN) was proposed without the noise level estimation [44]. Spatial spectral gradient network(SSGN) [45] for hybrid noise removal in hyperspectral imagery was proposed by consideration of the unique spatial structure directionality of sparse noise and spectral differences.…”

Section: Introductionmentioning

confidence: 99%

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Nonconvex Low Rank Approximation With Phase Congruency Regularization for Mixed Noise Removal

Luo

Wang

2019

IEEE Access

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Mixed noise removal from a natural image is a challenging task since the complex noise distribution usually is inestimable. Many noise removal methods based on the low rank approximation have an excellent image denoising performance and are effective for recovering the images corrupted by Gaussian noise. These methods based on the additive white Gaussian noise(AWGN) model are sensitive to the outliers and non-Gaussian noise, such as the salt-and-pepper impulse noise (SPIN) and random valued impulse noise (RVIN). Such methods for mixed noise removal, however, are less effective in preserving image structures and tend to undesired staircase artifacts. This paper presents a novel Nonconvex Low Rank Model with Phase congruency and overlapping Group sparsity regularization (NLRM-PG) for mixed noise removal. Moreover, an efficient optimization algorithm under the alternating direction method of multipliers and majorization minimization framework is proposed to solve the NLRM-PG model. We demonstrate that the proposed method is effective for preserving local irregular structures and it reduces staircase artifacts with the two types of mixture noise, namely, AWGN+SPIN and AWGN+RVIN. Both qualitative and quantitative experiment results on synthetic noisy images and real noisy images illustrate that the proposed method can remove mixture noise in images more efficiently than the existing methods can do. And the results also outperform those obtained by using the competing state-of-the-art methods, particularly for the removal of high-density impulse noise.INDEX TERMS Mixed noise removal, nonconvex low rank approximation, overlapping group sparsity, phase congruency.

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Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN

Cited by 26 publications

References 17 publications

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images

Image De-Noising With Machine Learning: A Review

Nonconvex Low Rank Approximation With Phase Congruency Regularization for Mixed Noise Removal

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