Methods for image denoising using convolutional neural network: a review

Ilesanmi, Ademola Enitan; Ilesanmi, Taiwo

doi:10.1007/s40747-021-00428-4

Cited by 165 publications

(80 citation statements)

References 125 publications

(122 reference statements)

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“…Denoising using CNNs has been the target of many studies 37 . While most of these models perform well on their dataset, they often have known noise amount and type in their images (e.g.…”

Section: Resultsmentioning

confidence: 99%

Reconstructing high fidelity digital rock images using deep convolutional neural networks

Bizhani

Ardakani

Little

2022

Sci Rep

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Imaging methods have broad applications in geosciences. Scanning electron microscopy (SEM) and micro-CT scanning have been applied for studying various geological problems. Despite significant advances in imaging capabilities, and image processing algorithms, acquiring high-quality data from images is still challenging and time-consuming. Obtaining a 3D representative volume for a tight rock sample takes days to weeks. Image artifacts such as noise further complicate the use of imaging methods for the determination of rock properties. In this study, we present applications of several convolutional neural networks (CNN) for rapid image denoising, deblurring and super-resolving digital rock images. Such an approach enables rapid imaging of larger samples, which in turn improves the statistical relevance of the subsequent analysis. We demonstrate the application of several CNNs for image restoration applicable to scientific imaging. The results show that images can be denoised without a priori knowledge of the noise with great confidence. Furthermore, we show how attaching several CNNs in an end-to-end fashion can improve the final quality of reconstruction. Our experiments with SEM and CT scan images of several rock types show image denoising, deblurring and super-resolution can be performed simultaneously.

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“…Denoising using CNNs has been the target of many studies 37 . While most of these models perform well on their dataset, they often have known noise amount and type in their images (e.g.…”

Section: Resultsmentioning

confidence: 99%

Reconstructing high fidelity digital rock images using deep convolutional neural networks

Bizhani

Ardakani

Little

2022

Sci Rep

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“…25 In this context, recent studies have shown the flexibility of machine learning approaches, especially deep learning techniques and their robustness for any type of noise. 26,27 Most important, the reconstruction speed of the deep learning-based approach outperforms conventional methods. 28 The objective of this study was not to compare denoising performances of different approaches but rather to take advantage of 1 solution that is clinically viable, due to almost instantaneous results, and to investigate its clinical validation.…”

Section: Discussionmentioning

confidence: 99%

Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging

Yamamoto¹,

Lacheret²,

Fukutomi³

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. MATERIALS AND METHODS:Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. RESULTS:Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence.CONCLUSIONS: Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.ABBREVIATIONS: AVD ¼ absolute volume difference; dDLR ¼ denoising using deep learning-based reconstruction; DSC ¼ Dice similarity coefficient; HD ¼ Hausdorff distance; MS ¼ multiple sclerosis; PPV ¼ positive predictive value; TPR ¼ true-positive rate M ultiple sclerosis (MS) is the most common inflammatory disease of the central nervous system affecting young patients, 1 in which demyelination mediated by autoimmune mechanisms is spatially and temporally disseminated. MR imaging plays an essential role not only in the initial diagnosis of MS 2 but also in regular monitoring as a sensitive marker of disease activity for promptly switching therapy if progression is observed. 3 Life-long imaging follow-up is, therefore, required for most patients with MS. A short examination time is necessary to improve the patient's comfort and to cope with the high number of demands in imaging centers.

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“…Generally speaking, the most commonly used methods for image restoration in computer vision are learned prior [ 27 ] and explicit prior [ 28 ]. Learned-prior is a simple method for training a deep convolution network to learn how to denoise images by training on a data set.…”

Section: Detailed System Architecture and Descriptionmentioning

confidence: 99%

An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

Arumugasamy

Bouazizi

Ohtsuki

2022

Sensors

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In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

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Methods for image denoising using convolutional neural network: a review

Cited by 165 publications

References 125 publications

Reconstructing high fidelity digital rock images using deep convolutional neural networks

Reconstructing high fidelity digital rock images using deep convolutional neural networks

Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging

An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

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