Comput. Sidorenko et al. Image denoising is an essential and fundamental pre-processing step for infrared imaging, X-ray imaging, Ultraviolet imaging, synthetic aperture radar imaging, multispectral remote sensing and almost all kinds of imaging along with their associated applications like military surveillance, medical imaging, biometrics, remote sensing, HVS (Human V. 43, 24802495. 4, 175187. Denoising Basics of Image Processing - GitHub Pages Water Resour. Data generation is achieved using a learnt, parametrized reverse process that performs iterative denoising, starting from pure random noise (see figure above). Geosci. Using an app designer, we can create an app that does this. (2019) trained a 20-layer feedforward neural network using synthetic data generated by the stochastic cross-correlation-based simulation (CCSIM) algorithm and found that neural networks perform better compared to bi-cubic interpolation for image super-resolution while the synthetic data further improved the model's generalizability. (2017). 4, 229240. Sci. Neural Netw. For example, a high CNR obtained for N2C makes it ideal for segmentation tasks while a high SSIM obtained through N2N-75% model makes it favorable for CT image artifact removal task. This can be explained by the blurring caused by the Gaussian filter at phase interfaces, making it more difficult to locate boundaries, especially where the gray-scale intensities are less distinguishable like at the solid-brine interfaces. MT, AA, YH, PP, RJ, PS, XH, and ZK: draft manuscript preparation. Water Resour. doi: 10.1002/2013WR014979.Reply, Larpudomlert, R., Torrealba, V. A., Karpyn, Z. T., and Halleck, P. (2014). IEEE Trans. In computer graphics, images can be made up of both useful information and noise. A fast two-dimensional median filtering algorithm. Fully supervised and fully unsupervised DL models account for the best and worst-case scenarios of data availability, respectively. 45, 103125. doi: 10.1016/j.advwatres.2016.01.010, Boas, F. E., and Fleischmann, D. (2012). Machine learning and deep learning reconstruction uses a neural network to reconstruct the signal. Table 1. For blind DnCNN, the networks are trained with noise levels from 0 to 55, which is not perfect for other noise levels. 8, 189193. The scCO2-solid phase boundary has the most improvement in boundary sharpness with ~28% increase in slope. Figure 4. Residual dense network, a very deep network, could be overfitting the data and therefore less able to adjust to the varying noise along the sample, compared to other supervised models (N2C and CCGAN). Imaging and measurement of pore-scale interfacial curvature to determine capillary pressure simultaneously with relative permeability. This is mainly due to the aluminum container, covering the bottom half of the sample, used to fix the coreholder in place during the scan and avoid wobbling which can result in image blurring (Supplementary Figure 1B). Oil Gas Sci. You can adjust the noise removal using two basic sliders to remove noise and sharpen. Using these models, we investigate the optimal number of high-exposure reference images that balances data acquisition cost and accurate petrophysical characterization. Learn more Contact DeNoize Harmony Glass DeNoize harmony glass is an active noise canceling kit that fits onto any existing window frame. Meaning of denoising. (A), HQ reference; (B), LQ; (C),Gaussian; (D), Median; (E), Non-local means; (F), Anisotropic diffusion; (G), Bilateral; (H), Symmetric nearest neighbor; (I), Noise-to-clean; (J), Residual dense network; (K), Cycle consistent generative adversarial network; (L), Noise-to-void; (M), Noise-to-noise (75%); (N), Noise-to-noise (50%); (O), Noise-to-noise (25%); (P),Noise-to-noise. Eng. (2021) implemented an encoder-decoder network for denoising MCT images obtained from tight sandstone samples. 6, 185208. SPE Reserv. (2019). A cross-sectional image for each of the HQ dataset, LQ dataset and the denoised datasets are shown in Figure 2. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Out of the traditional filters, SNN followed by median filters showed the most deviation from the ground truth. Adv Water Resour. Phys Rev E. 94, 110. Imaging. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. J. That is, if the difference between the target voxel value and the value of its neighboring six voxels exceeds this stop threshold value, diffusion will not occur, hence preserving and enhancing phase interfaces. Water Resour Res. By installing DeNoize harmony glass on your window you are able to [] doi: 10.2118/11758-PA, Wang, Y. D., Blunt, M. J., Armstrong, R. T., and Mostaghimi, P. (2021). Cromwell, V., Kortum, D. J., and Bradley, D. J. Other methods like RDN and N2N25 perform poorly with errors as large as 348.28%. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. Experimental Investigation of Pressure Dependence of Contact Angle in CO2-Brine Systems. Dissertation, Pennsylvania State University. The most prevalent noise forms follow Poisson and Gaussian distributions. Residual dense networks have lately shown that accumulating features through local and global residual learning can significantly improve image restoration (Zhang et al., 2021). We also compare computational resource requirements and ease of implementation of the different methods and provide recommendations. Since the radiation-sensitive property of medical images, various noises occur during the acquisition process, especially when radiation dose reduces. We also observe one exception where RDN performs better than N2C and N2N75 in terms of brine SSA. 9, 1659116603. Eval. Further details about the architecture, input requirements and choice of hyper-parameters like batch sizes, learning rate and number of epochs are provided in Table 4. In general, most traditional filters perform better for bulk estimates of porosity and phase saturation, compared to DL-based methods. Radiation Detection and Measurement, 3rd Edn. 10 Articles, This article is part of the Research Topic, https://www.corelab.com/cli/core-holders/x-ray-core-holder-fch-series, http://www.scikit-video.org/stable/modules/generated, https://www.frontiersin.org/articles/10.3389/frwa.2021.800369/full#supplementary-material, https://www.mia.uni-saarland.de/weickert/Papers/book.pdf, Creative Commons Attribution License (CC BY). Deep learning denoising by dimension reduction: Application to the 53, 1027410292. A summary of findings from select literature on image denoising with applications in digital rock physics is provided in Table 1. doi: 10.1109/CVPR.2005.38, Budrikis, Z. L (1972). Finally, we compare the quantitative petrophysical results of the denoised images post segmentation against the ground truth HQ segmented images. A median filter is also a low-pass smoothening filter. The accuracy with which we can estimate these pore-scale properties affect our ability to explain and predict multiphase fluid flow in porous media. Computations of absolute permeability on micro-CT images. (2018). Qualitative comparison of the performance of the different denoising methods through example cross-sectional slices. Introduction The appearance of noise is random and inevitable, which is closely related to image quality assessment. Two datasets were acquired at the same voxel resolution of 15 m: a high-quality (HQ) scan, where more exposure time was allowed to reduce noise. Optimize different denoising methods to accommodate different types of datasets. Figure 6 shows example segmented cross sections from the HQ and LQ datasets. Modeling geometric state for fluids in porous media: evolution of the euler characteristic. Image denoising is an essential and fundamental pre-processing step for infrared imaging, X-ray imaging, Ultraviolet imaging, synthetic aperture radar imaging, multispectral remote sensing and almost all kinds of imaging along with their associated applications like military surveillance, medical imaging, biometrics, remote sensing, HVS (Human . U-Nets have the ability to accumulate hierarchical features at multiple resolutions and maintain sharpness throughout the decoding process (Diwakar and Kumar, 2018). The brain-computer interface (BCI) has many applications in various fields. The differences observed, however, between supervised DL-based models and N2N-75% in terms of SSIM are not statistically significant, meaning that they all perform equally well for this metric. The HQ data is not an idealistic ground truth data, it has its own noise associated with it. Sensors 21, 117. For the pore-scale properties, we observe that supervised and semi-supervised methods show a better performance overall, except for scCO2 SSA and average pore size, where bilateral and SNN filters, respectively, show better performance. N2N75, which is a newly proposed semi-supervised variation of the N2N model, where 75% of the clean reference data is used for training, shows very promising outcomes for both traditional denoising performance metrics and petrophysical properties including both bulk and pore-scale measures. doi: 10.1109/TIP.2003.819861, Weickert, J (1996). Noise2void-learning denoising from single noisy images, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Long Beach, CA), 21242132. Pore-scale determination of gas relative permeability in hydrate-bearing sediments using, x-ray computed micro-tomography and lattice boltzmann method. image finds applications in computer vision areas where the general shape or outline information of the image is needed. Improved Blind Image Denoising with DnCNN | SpringerLink Noise2Noise: learning image restoration without clean data, in Proceedings of the 35th International Conference on Machine Learning, Vol. From a Bayesian perspective, the MAP probability estimate of x is. (1990). The porous medium is saturated with high salinity brine that has a composition representative of a saline aquifer (Tawfik et al., 2019). But it isnt just photographs that can look discolored noise is common in computer graphics, too. Also, the number of markers was kept to a minimum (~0.005% of sample voxels) to avoid overfitting. doi: 10.1016/j.advwatres.2015.11.006, Knoll, G. F (2000). Audio Denoiser: A Speech Enhancement Deep Learning Model - Analytics Vidhya We perform training over 200 epochs and use the ADAM optimization algorithm for all the models. For our purpose, we use a mixture of Poisson-Gaussian along with Perlin noise textures to simulate random noise. (Left) Example cross-sections through the high quality (HQ), low quality (LQ) and Noise-to-Clean (N2C) datasets. The AD filter (Weickert, 1996) also known as the Perona-Malik diffusion, aims to reduce image noise within each phase while preserving edges and boundaries between phases. 2:7. doi: 10.1186/s42492-019-0016-7, Freire-Gormaly, M., Ellis, J. S., MacLean, H. L., and Bazylak, A. Digital rock physics benchmarks-part II: computing effective properties. For example, pore-network modeling, which is used in a variety of digital rock studies to explain and predict macroscopic transport properties such as absolute permeability, relative permeability, and capillary pressure, uses simplified pore structures composed of a network of pores and throats, which can be directly extracted from the MCT images (Valvatne and Blunt, 2004; Jia et al., 2007; Dong et al., 2009; Mostaghimi et al., 2013; Berg et al., 2016; Zahaf et al., 2017; Raeini et al., 2019). The DL models tend to produce images that appear cleaner than the HQ image but score lower PSNR due to this reason. Use idealized simulated or synthetic ground truth reference images and test the effect of different types and levels of noise on the performance of each of the denoising methods, as well as combinations of the denoising methods. doi: 10.1038/s41598-018-36047-2, Berg, S., Rcker, M., Ott, H., Georgiadisa, A., van der Lindea, H., Enzmannb, F., et al. They are also referred to as 1 bit/pixel images. 60, 8490. We derive the recommendations from Supplementary Table 1 and Figure 8 where we illustrate the superiority of some algorithms over others. Further, the differences within this single image might seem subtle, but it has a significant effect on petrophysical characterization performed using the entire image stack. A large enough search window is selected to increase the chances of finding similar structures and phases. doi: 10.1016/j.jcis.2006.08.048, Purswani, P., Karpyn, Z. T., Enab, K., Xue, Y., and Huang, X. It ranges from 0 to 1, where higher SSIM indicates a cleaner image. Int. Denoising makes the image more clear and enables us to see finer details in the image clearly. A pore-network-modeling approach to predict petrophysical properties of diatomaceous reservoir rock. 22, 114. Res. Common architectures are used for supervised and unsupervised methods. Technol. Recently, Alsamadony et al. A summary of selected publications that report on MCT imaging in the digital rock physics domain, highlighting the commonly used denoising methods. MT, ZK, and PP: data collection. Compensation of drift in an industrial computed tomography system, 6th Conference on Industrial Computed Tomography (Wels), 16. The motivation for variational denoising methods of Eq. The scanning details for both datasets are presented in Table 3. Some hyperparameters are optimized to obtain the best possible performance from the model. Noise-to-noise ratio also shows a surprisingly close match to the ground truth brine fraction profile. Eval. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Scale-space and edge detection using anisotropic diffusion. The histogram representing the distribution of gray-scale intensities within a dataset can be used to qualitatively assess image quality. Though the unsupervised models performed poorly, they were comparable to the traditional filter-based methods. The implementation of BI is detailed in the Appendix in Supplementary Material. We also observe slightly higher average intensity for the solid and brine phases in the HQ profile compared to the LQ profile. doi: 10.2118/20495-PA, Jia, L., Ross, C. M., and Kovscek, A. R. (2007). 37, 13481357. Commonly used denoising filters in the digital rock physics literature, namely NLM and AD, show reasonable performance in terms of traditional denoising metrics like PSNR and SSIM. Raeini, A. Q., Yang, J., Bondino, I., Bultreys, T., Blunt, M. J., and Bijeljic, B. A New Wavelet Threshold Function and Denoising Application - Hindawi doi: 10.1007/s11242-020-01420-1, Menke, H. P., Andrew, M. G., Blunt, M. J., and Bijeljic, B. Such profiles have been shown to emulate low-exposure noise in CT images (Lee et al., 2018). 2. 24, 5355. We pick the epoch with the lowest validation PSNR in order to perform testing. However, the discussion on image denoising was brief. Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study. PDF arXiv:1912.13171v4 [eess.IV] 3 Aug 2020 40, 121. In the forward diffusion process, gaussian noise is introduced successively until the data becomes all noise. For the Perlin noise, we use a simplex model with scale set to 5, octaves to 6, persistence to 0.5, and lacunarity to 2.0 which showed the best match for the texture. Section 2 summarizes the related work on image denoising. Beyond Darcy's law: the role of phase topology and ganglion dynamics for two-fluid flow. Tawfik, M. S., Karpyn, Z., and Johns, R. (2019). The lack of quantitative metrics, both standard and physics-based, cause errors during this critical image processing step to remain largely unquantified. Deep learning (DL) powered computer vision has seen phenomenal improvement since the introduction of AlexNet in 2012 (Krizhevsky et al., 2017). Sci. Water Resour. Theoretically, PSNR for the HQ image is not defined and so it is manually set to 100 dB. J Colloid Interface Sci. Huang, T. S., Yang, G. J., and Tang, G. Y. We also observe that some denoising models possibly outperformed the HQ dataset where the three peaks are even more distinguishable, including N2C, RDN, CCGAN, and N2N75. The advantage of spatial filtering is that it doesnt produce temporal lag, which is the inability to immediately respond to changing flow conditions. They demonstrated that DL-based image processing can improve image quality; and that pre-trained VDSR network with fine-tuning tends to out-perform VDSR trained from scratch. Adv. ^Ilastik (2021). Kamrava et al. Improvements in the DL denoising field result from a clearer understanding of the specific noise forms and improvements in the architecture of neural networks. Image denoising finds applications in fields such as astronomy where the resolution limitations are severe, in medical imaging where the physical requirements for high . This can also be useful in cases where the phenomenon being investigated has a short time-scale like chemical processes and pore-scale flow events. In this section, we briefly describe the experiment conducted by Niu (2021) to generate the datasets used in this study. SPE Form. Rep. 8, 17. Image quality assessment: from error visibility to structural similarity. Denoising | Papers With Code Dynamics of snap-off and pore-filling events during two-phase fluid flow in permeable media. These complex conditions necessitate that the denoiser adjust . . Kuala Lumpur: Springer International Publishing. 1. Overall, CCGAN is found to be the least computationally efficient, whereas N2C, N2N%, and N2N are the most efficient. 375, 187192. Figure 7. Counter-intuitively, adding additional information in low quantities (e.g., N2N25) can sometimes affect the model's capability to converge, leading to larger errors, as seen when we compare N2N and N2N25 errors for most properties. Finally, we discuss the state-of-the-art methods for image denoising, . Optimize the newly proposed semi-supervised denoising models to determine the optimum threshold percentage of HQ high-exposure images that are needed while maintaining high accuracy of petrophysical analysis. Meinhardt et al., 2017) was presented for image applications, such as image denoising. Features selected for segmentation for the different datasets using ilastik. doi: 10.1007/s10596-009-9130-5. doi: 10.1029/2018GL081784, Scanziani, A., Singh, K., Blunt, M. J., and Guadagnini, A. The authors thank the financial support of the National Energy Technology Laboratory's ongoing research under the RSS contract number 89243318CFE000003. What does denoising mean? To decide which denoising methods perform better in terms of bulk properties (porosity and scCO2 saturation) and pore-scale properties (SEN, SSA, interfacial area, average pore size and average blob volume), we sum up the absolute errors for each of the denoising methods for those two groups of properties. A prior-guided deep network for real image denoising and its applications The following conclusions are derived based on the analysis of our results. Peak signal to noise ratio is a comparative metric widely used to determine noise degradation in an image with respect to a clean reference. Low-rank approximation of Hankel matrices in denoising applications for An application of the Hankel matrix denoising to subspace-based damage detection is given is Section 5.2. Properties of the porous core sample (Niu, 2021). Biomed. Introduction to Radiological Physics and Radiation Dosimetry. Another important factor to consider when selecting a DL-based denoising model is computational requirements. doi: 10.1016/j.jngse.2017.06.018, Kamrava, S., Tahmasebi, P., and Sahimi, M. (2019). Similarly, NLM, which is one of the most commonly used filters in the digital rock physics literature, underestimates porosity throughout the length of the core and has the second worst performance. The neighborhood is pre-specified by the user as 6 faces, 18 edges, or 26 vertices. The granularity and texture of the noise profile in low quality (LQ) CT images can be captured by Perlin noise. 1. The histogram indicates the distribution of pixel intensities but not their location. Similarly, the blue region shows an example where the solid phase can be falsely characterized as connected in the LQ image, while the HQ image shows some disconnection of the solid phase. Al-Menhali, A. S., Menke, H. P., Blunt, M. J., and Krevor, S. C. (2016). With Blur Index however, the metric measures the presence of sharpness which can be spiked by high frequency noise. Sommer, C., Straehle, C., Kothe, U., and Hamprecht, F. A. 12, 629639. Table 4. Geosci. Though noise in MCT has not been fully profiled, many studies including Diwakar and Kumar (2018) and Lee et al. Though these factors influence the choice of a model individually, we list combinations of those factors to offer model recommendations for different scenarios. Next, we present quantitative comparison results. Prod. Figure 8 shows the comparison of computational expenditure for the different DL-based models.

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