Multi‐scale Cross‐path Concatenation Residual Network for Poisson denoising

Su, Yueming; Lian, Qiusheng; Zhang, Xiaohua; Shi, Baoshun; Fan, Xiaoyu

doi:10.1049/iet-ipr.2018.5941

Cited by 21 publications

(16 citation statements)

References 30 publications

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“…Su at al. [78] have proposed a novel method to tackle the problems caused due to Poisson noise in the low-light imaging field. This proposal is that of a deep multi-scale cross-path concatenation residual network (MC2RNet) which incorporates cross-path concatenation modules for denoising.…”

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

confidence: 99%

Image De-Noising With Machine Learning: A Review

et al. 2021

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“…Dilated convolutions allow the algorithm to explicitly modify the receptive field of view size from convolution filters [23] without the burden of stacking large convolution operations. A combination of multi‐scale with dilated convolutions for a 2D domain was done in [24]. For our purpose, we extend the 3D multi‐branch convolutions with dilated convolutions.…”

Section: Methodsmentioning

confidence: 99%

Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view

et al. 2019

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In this study, the authors propose a novel three‐dimensional (3D) convolutional neural network for shape reconstruction via a trilateral convolutional neural network (Tri‐CNN) from a single depth view. The proposed approach produces a 3D voxel representation of an object, derived from a partial object surface in a single depth image. The proposed Tri‐CNN combines three dilated convolutions in 3D to expand the convolutional receptive field more efficiently to learn shape reconstructions. To evaluate the proposed Tri‐CNN in terms of reconstruction performance, the publicly available ShapeNet and Big Data for Grasp Planning data sets are utilised. The reconstruction performance was evaluated against four conventional deep learning approaches: namely, fully connected convolutional neural network, baseline CNN, autoencoder CNN, and a generative adversarial reconstruction network. The proposed experimental results show that Tri‐CNN produces superior reconstruction results in terms of intersection over union values and Brier scores with significantly less number of model parameters and memory.

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“…Deep neural network denoising techniques have drawn a lot of attention (Buchholz et al, 2019a,b;Chang et al, 2019;Guo et al, 2018;Kadimesetty et al, 2018;Kokkinos and Lefkimmiatis, 2019;Lehtinen et al, 2018;Lin et al, 2019;Liu et al, 2018;Mildenhall et al, 2018;Ran et al, 2019;Song et al, 2019;Su et al, 2019;Xie et al, 2018; as they have significant impacts in addressing several drawbacks in conventional analytical methods (Lucas et al, 2018) such as (1) computation burden in the testing phase, i.e., an analytical method requires to resolve an optimization problem for every input, which is computationally inefficient, and (2) difficulties in setting up hyper-parameters to incorporate prior or domain knowledge. Deep Convolutional Neural Networks (DCNNs) are the default models of the choice when working with highly structured datasets such as images and videos, as DCNNs are (1) more computationally efficient than multilayer perceptron models featuring fewer parameters, and (2) take the advantages of the structured datasets such as translation invariance and locality.…”

Section: Introductionmentioning

confidence: 99%

Noise2Atom: Unsupervised Denoising for Scanning Transmission Electron Microscopy Images

Wang

Henninen

Keller

et al. 2020

Preprint

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We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain S to a target domain C, where S is for our noisy experimental dataset, and C is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

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Multi‐scale Cross‐path Concatenation Residual Network for Poisson denoising

Cited by 21 publications

References 30 publications

Image De-Noising With Machine Learning: A Review

Image De-Noising With Machine Learning: A Review

Trilateral convolutional neural network for 3D shape reconstruction of objects from a single depth view

Noise2Atom: Unsupervised Denoising for Scanning Transmission Electron Microscopy Images

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