A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image

Zhang, Yushu; Lin, Hongbo; Li, Yue; Ma, Haitao

doi:10.1109/access.2019.2949774

Cited by 40 publications

(15 citation statements)

References 38 publications

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“…The projection domain filtering algorithm filters the original data in the projection domain and then uses FBP to reconstruct CT images. Typical methods include bilateral filtering method [ 13 ], adaptive convolution filtering method [ 14 ], penalty weighted least square (PWLS) method [ 15 ], and so on.…”

Section: Related Workmentioning

confidence: 99%

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network

Liu

2021

Journal of Healthcare Engineering

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Low-dose computed tomography (CT) has proved effective in lowering radiation risk for the patients, but the resultant noise and bar artifacts in CT images can be a disturbance for medical diagnosis. The difficulty of modeling statistical features in the image domain makes it impossible for the existing methods that directly process reconstructed images to maintain the detailed texture structure of images while reducing noise, which accounts for the failure in CT diagnostic images in practical application. To overcome this defect, this paper proposes a CT image-denoising method based on an improved residual encoder-decoder network. Firstly, in our approach, the notion of recursion is integrated into the original residual encoder-decoder network to lower the algorithm complexity and boost efficiency in image denoising. The original CT images and the postrecursion result graph output after recursion are used as the input for the next recursion simultaneously, and the shallow encoder-decoder network is recycled. Secondly, the root-mean-square error loss function and perceptual loss function are introduced to ensure the texture of denoised CT images. On this basis, the tissue processing technology based on clustering segmentation is optimized considering that the images after improved RED-CNN training will still have certain artifacts. Finally, the experimental results of the TCGA-COAD clinical data set show that under the same experimental conditions, our method outperforms WGAN in average postdenoising PSNR and SSIM of CT images. Moreover, with a lower algorithm complexity and shorter execution time, our method is a significant improvement on RED-CNN and is applicable for actual scenarios.

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Section: Related Workmentioning

confidence: 99%

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network

Liu

2021

Journal of Healthcare Engineering

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“…The authors from [40][41][42][43], shown that the technics of denoising image rely on the transform domain and spatial domain. In this part, we based of work done in [44] and present the FCNN architecture used to applied in our work. Through mathematical explanation, the parameter update process in the CNN architecture is introduced in detail.…”

Section: Image Denoising Theory In Ct Scan: Overviewmentioning

confidence: 99%

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Takam¹,

Kouanou²,

Samba³

et al. 2021

Artificial Intelligence

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Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.

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“…Therefore, the extracted features are difficult to match the test sets exactly, which affects the denoising performance of the neural network. In addition, even if the label‐based machine learning methods produce the good denoised results, it will take a lot of work in making complex and large of training sets (Zhu et al ., 2019; Zhang et al ., 2019b). It is of great importance to develop the unsupervised machine learning method to attenuate random noise.…”

Section: Introductionmentioning

confidence: 99%

“…It can obtain valuable information from the data by learning the abstract representation in the form of combination of multiple layers (LeCun et al ., 2015). Currently, label‐based machine learning approaches have been successfully applied to suppress seismic data noise (Zhao et al ., 2018; Liu et al ., 2019; Zhang et al ., 2019b; Si and Yuan, 2018; Liu et al ., 2018; Jin et al ., 2018). According to the strategy for generating labels, these approaches can mainly be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

Gao

Zhao²,

et al. 2021

Geophysical Prospecting

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Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the following steps: Firstly, we randomly mute a part of the input data of the neural network according to a certain percentage, and then the network outputs the reconstructed data influenced by this randomly mute. The objective function measures the distance between the input data and the reconstructed data. Secondly, we use the adaptive moment estimation algorithm to minimize the distance, and the network adjusts its internal parameters so that sparse representations can be captured by the multiple processing layers of the neural network. Finally, we take the same proportion of random mute on the raw seismic data which are fed to the trained neural network. Through this network, reconstruction of seismic data and attenuation of random noise are completed simultaneously. We use both synthetic and field data to testify the feasibility and applicability of the proposed method. Synthetic data experiment indicates that the proposed method achieves better denoised results than the conventional methods. Field data applications further demonstrate its superiority and practicality.

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A Patch Based Denoising Method Using Deep Convolutional Neural Network for Seismic Image

Cited by 40 publications

References 38 publications

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network

A Method of CT Image Denoising Based on Residual Encoder-Decoder Network

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

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