DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images

Chen, Zailiang; Zeng, Ziyang; Shen, Hailan; Zheng, Xinran; Dai, Peishan; Ouyang, Pingbo

doi:10.1016/j.bspc.2019.101632

Cited by 92 publications

(46 citation statements)

References 26 publications

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“…For other OCT GAN based denoising methods, Ma et al employed a U‐Net based structure for the generator design that is difficult to train a deep neural network without information loss. Huang et al and Chen et al used a DenseNet based generator network, which includes fewer training parameters. However, the DenseNet utilized much larger GPU memory and computational power compared to the ResNet based networks .…”

Section: Discussionmentioning

confidence: 99%

“…However, the DenseNet utilized much larger GPU memory and computational power compared to the ResNet based networks . In addition, different from Chen et al , both the generator and discriminator networks of our SM‐GAN employed Leaky Relu layers . Leaky Relu layers can accelerate the training process and keep the backpropagation optimization from getting stuck .…”

Section: Discussionmentioning

confidence: 99%

“…Generative adversarial network (GAN) is a promising deep neural network, which has been used in different machine learning problems, including text to image transformation , image generation and image editing . Recently, Huang et al , Ma et al and Chen et al employed the GAN method to denoise OCT images for ophthalmic applications. With different GAN network structures, more general datasets and better training strategies, the GAN method for OCT image denoising can be further improved.…”

Section: Introductionmentioning

confidence: 99%

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Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Zhang

Liu

et al. 2020

Journal of Biophotonics

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Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle-modulating OCT (SM-OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM-OCT images were used for training and validating the neural network model, which we call SM-GAN. The performance of the SM-GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM-GAN model was compared to traditional OCT denoising methods and other stateof-the-art deep learning based denoise networks. We conclude that the SM-GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Zhang

Liu

et al. 2020

Journal of Biophotonics

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“…EstimatingX from X is an inverse problem and the challenge is how to model the function that is able to generate E, so that X can be estimated. Many studies have been conducted to estimate E. Recently, GAN has commonly used and proven its effectively to solve this task [28][29][30][31][32][33].…”

Section: Proposed Methodsmentioning

confidence: 99%

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Mukaroh

Kim

2020

Sensors

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Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

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“…Moreover, recently a new generation of neural networks has been proposed that is commonly named Generative Adversarial Networks (GANs) (Goodfellow et al, 2014). These networks could be trained for complex image-to-image translation tasks such as object transfiguration (Zhu et al, 2017), image superresolution (Zhu et al, 2017) and noise reduction (Chen et al, 2020). A GAN model consists of two networks: a generator G and a discriminator D. Two networks are trained simultaneously for concurrent tasks.…”

Section: Introductionmentioning

confidence: 99%

Dense 3d Object Reconstruction Using Structured-Light Scanner and Deep Learning

Kniaz

Mizginov

Grodzitkiy

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Structured light scanners are intensively exploited in various applications such as non-destructive quality control at an assembly line, optical metrology, and cultural heritage documentation. While more than 20 companies develop commercially available structured light scanners, structured light technology accuracy has limitations for fast systems. Model surface discrepancies often present if the texture of the object has severe changes in brightness or reflective properties of its texture. The primary source of such discrepancies is errors in the stereo matching caused by complex surface texture. These errors result in ridge-like structures on the surface of the reconstructed 3D model. This paper is focused on the development of a deep neural network LineMatchGAN for error reduction in 3D models produced by a structured light scanner. We use the pix2pix model as a starting point for our research. The aim of our LineMatchGAN is a refinement of the rough optical flow A and generation of an error-free optical flow B̂. We collected a dataset (which we term ZebraScan) consisting of 500 samples to train our LineMatchGAN model. Each sample includes image sequences (Sl, Sr), ground-truth optical flow B and a ground-truth 3D model. We evaluate our LineMatchGAN on a test split of our ZebraScan dataset that includes 50 samples. The evaluation proves that our LineMatchGAN improves the stereo matching accuracy (optical flow end point error, EPE) from 0.05 pixels to 0.01 pixels.

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DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images

Cited by 92 publications

References 26 publications

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Dense 3d Object Reconstruction Using Structured-Light Scanner and Deep Learning

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