Deep convolutional architecture for natural image denoising

Wang, Xuejiao; Tao, Qiuyan; Wang, Lianghao; Li, Dongxiao; Zhang, Ming

doi:10.1109/wcsp.2015.7341021

Cited by 19 publications

(16 citation statements)

References 9 publications

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“…Their network could accept an image as the input and produce an entire image as an output through four hidden layers of convolutional filters. Wang et al [26] proposed a deep convolutional architecture for natural image denoising to overcome the limitation of fixed image size in deep learning methods. Their architecture is a modified CNN structure with rectified linear units and local response normalization, and the sampling rate of all pooling layers was set to one.…”

Section: Related Workmentioning

confidence: 99%

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Fully Symmetric Convolutional Network for Effective Image Denoising

Priyanka

Wang

2019

Applied Sciences

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Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional–deconvolutional layers. This framework learns convolutional–deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstractions to recover the image content details. An adaptive moment optimizer is used to minimize the reconstruction loss as it is appropriate for large data and noisy images. Extensive experiments were conducted for image denoising to evaluate the FSCN model against the existing state-of-the-art denoising algorithms. The results show that the proposed model achieves superior denoising, both qualitatively and quantitatively. This work also presents the efficient implementation of the FSCN model by using GPU computing which makes it easy and attractive for practical denoising applications.

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Section: Related Workmentioning

confidence: 99%

“…An Adam [34] optimizer is adapted to optimize architecture with learning rate 0.001. It should be noted that the learning rate for all layers is the same, unlike in other approaches [26] in which a smaller learning rate is set for the last layer.…”

Section: Trainingmentioning

confidence: 99%

Fully Symmetric Convolutional Network for Effective Image Denoising

Priyanka

Wang

2019

Applied Sciences

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“…Several CNN designs were applied to denoising tasks for both natural images [10][11][12] and medical data. 8 Wang et al 10 proposed a CNN architecture for natural image denoising, with a linear structure composed of a sequence of four convolutional layers without pooling layers, whereas a rectified linear unit (ReLU) was used as the activation function. The second to last layer is a fully connected layer defined as a convolution layer with only one kernel, so only one image is obtained.…”

Section: Cnn-based Image Denoisingmentioning

confidence: 99%

“…Moreover, the needed image processing time is extremely low compared with the other two methods. 10 A study on low-dose CT image denoising was proposed by Chen et al, 13 simulated by adding Poisson noise to normal-dose CT images. The CNN architecture includes three convolutional layers without pooling and employed ReLU as an activation function.…”

Section: Cnn-based Image Denoisingmentioning

confidence: 99%

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Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention

et al. 2018

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Percutaneous coronary intervention (PCI) uses x-ray images, which may give high radiation dose and high concentrations of contrast media, leading to the risk of radiation-induced injury and nephropathy. These drawbacks can be reduced by using lower doses of x-rays and contrast media, with the disadvantage of noisier PCI images with less contrast. Vessel-edge-preserving convolutional neural networks (CNN) were designed to denoise simulated low x-ray dose PCI images, created by adding artificial noise to high-dose images. Objective functions of the designed CNNs have been optimized to achieve an edge-preserving effect of vessel walls, and the results of the proposed objective functions were evaluated qualitatively and quantitatively. Finally, the proposed CNN-based method was compared with two state-of-the-art denoising methods: K-SVD and block-matching and 3D filtering. The results showed promising performance of the proposed CNN-based method for PCI image enhancement with interesting capabilities of CNNs for real-time denoising and contrast enhancement tasks.

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Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

Pižurica¹

2017

Wiley Encyclopedia of Electrical and Electronics Engineering

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This article presents an overview of image denoising algorithms ranging from wavelet shrinkage to patch‐based nonlocal processing. The focus is on the suppression of additive Gaussian noise (white and colored). A great attention is devoted to explaining the main underlying ideas and concepts of representative approaches, complemented by illustrative examples, accessible also to nonexperts in the field. A Bayesian perspective of wavelet shrinkage is given, with different instances of spatial context modeling (including local spatial activity indicators, Markov random fields, hidden Markov tree models, and Gaussian scale mixture models). Extensions to other transform domains (curvelets and other generalizations of wavelets) are addressed too, showing the benefits in terms of image quality. Patch‐based image denoising is illustrated with principles of nonlocal means filtering and collaborative filtering, explaining also the connections with dictionary learning. Some general notes on the performance comparison are given, by summarizing the benefits and limitations of various approaches against each other, and pointing to some of the current trends in the field.

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Deep convolutional architecture for natural image denoising

Cited by 19 publications

References 9 publications

Fully Symmetric Convolutional Network for Effective Image Denoising

Fully Symmetric Convolutional Network for Effective Image Denoising

Convolutional neural network-based image enhancement for x-ray percutaneous coronary intervention

Image Denoising Algorithms: From Wavelet Shrinkage to Nonlocal Collaborative Filtering

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