Toward Universal Stripe Removal via Wavelet-Based Deep Convolutional Neural Network

Chang, Yi; Chen, Meiya; Yan, Luxin; Zhao, Xi-Le; Li, Yi; Zhong, Sheng

doi:10.1109/tgrs.2019.2957153

Cited by 64 publications

(45 citation statements)

References 69 publications

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“…Later on, these networks started getting used for super-resolution (SR) imaging [93] and some of the first applications focused on photographs (see, e.g., [93][94][95][96]) and movies [97]. They were rapidly used for the resolution enhancement of, for example, satellite images [98,99] and medical images [100], like magnetic resonance imaging [101,102] and CT [103][104][105][106][107]. The success of a lot of these SR applications is explained by the emergence of generative adversarial networks (GANs) [108] which are commonly known for their strength at generating realistic "fake" images [109].…”

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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Fluid flow characteristics are important to assess reservoir performance. Unfortunately, laboratory techniques are inadequate to know these characteristics, which is why numerical methods were developed. Such methods often use computed tomography (CT) scans as input but this technique is plagued by a resolution versus sample size trade-off. Therefore, a super-resolution method using generative adversarial neural networks (GANs) was used to artificially improve the resolution. Firstly, the influence of resolution on pore network properties and single-phase, unsaturated, and two-phase flow was analysed to verify that pores and pore throats become larger on average and surface area decreases with worsening resolution. These observations are reflected in increasingly overestimated single-phase permeability, less moisture uptake at lower capillary pressures, and high residual oil fraction after waterflooding. Therefore, the super-resolution GANs were developed which take low (12 µm) resolution input and increase the resolution to 4 µm, which is compared to the expected high-resolution output. These results better predicted pore network properties and fluid flow properties despite the overestimation of porosity. Relevant small pores and pore surfaces are better resolved thus providing better estimates of unsaturated and two-phase flow which can be heavily influenced by flow along pore boundaries and through smaller pores. This study presents the second case in which GANs were applied to a super-resolution problem on geological materials, but it is the first one to apply it directly on raw CT images and to determine the actual impact of a super-resolution method on fluid predictions.Materials 2020, 13, 1397 2 of 33 examples requires detailed knowledge on fluid storage and flow properties which could be estimated through experiments, either carried out in specialized laboratories or deduced from digital rocks. Laboratory measurements, however, require expensive and complex set-ups, take a long time to complete, are often destructive, do not always entirely capture the studied properties and on top of that, experiments often fail to produce reproducible results between laboratories [9,[12][13][14][15][16][17][18][19]. Therefore, numerical solutions have been developed allowing to compute fluid storage and flow properties on digital rocks [7,[20][21][22][23]. Such digital rocks are acquired through direct imaging or numerical simulations of rocks.Computed tomography (CT) is a technique to rapidly capture the three-dimensional structure of materials [22,24,25]. However, like any imaging technique, CT is plagued by a sample size versus resolution trade-off, which is adversely affecting fluid flow simulations. Both a high resolution and sufficiently large volume are required for accurate fluid flow estimations at the pore-scale [9,[26][27][28]. Resolution impacts how well pore network topology and pore surfaces are resolved. At lower resolution, i.e., lower resolving power, pore sizes are often overestimated and specific surf...

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confidence: 99%

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

2020

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“…So for these deep-learning-based methods, there may be two aspects worth considering to upgrade their performance; namely, enhancing the discrimination of stripe noise and the comprehension of image content [35]. The concrete challenges could include a realistic simulation model of stripe noise, a particular objective function (with some prior knowledge about the noise and image), an elaborate network architecture and so on.…”

Section: Related Workmentioning

confidence: 99%

“…As an alternative for destriping, transform domain methods that are chiefly designed and realized in the Fourier domain and wavelet domain have also received considerable attention and research [35][36][37][38][39][40][41][42][43][44]. One significant starting point of these methods is that stripe noise has a concentrated energy distribution in both domains due to its directionality and global similarity.…”

Section: Related Workmentioning

confidence: 99%

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Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

2020

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Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method.

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“…Research is also being conducted using a scene-based non-uniform correction (SBNUC) method [1] to remove line noise that is often observed in HS systems. Also, several CNN-based methods have been studied to solve these problems [14], [15], [16]. Among them, the two-stream wavelet enhanced U-net (TSWEU) [16] method analyzed noises of various line patterns and has shown observable performance.…”

Section: Related Workmentioning

confidence: 99%

Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

Jung

Kim

2020

IEEE Access

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Toward Universal Stripe Removal via Wavelet-Based Deep Convolutional Neural Network

Cited by 64 publications

References 69 publications

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Computed Tomography 3D Super-Resolution with Generative Adversarial Neural Networks: Implications on Unsaturated and Two-Phase Fluid Flow

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

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