Journal articles: 'Denoisers'

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Gu, Jeongmin, Jose A. Iglesias-Guitian, and Bochang Moon. "Neural James-Stein Combiner for Unbiased and Biased Renderings." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–14. http://dx.doi.org/10.1145/3550454.3555496.

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Unbiased rendering algorithms such as path tracing produce accurate images given a huge number of samples, but in practice, the techniques often leave visually distracting artifacts (i.e., noise) in their rendered images due to a limited time budget. A favored approach for mitigating the noise problem is applying learning-based denoisers to unbiased but noisy rendered images and suppressing the noise while preserving image details. However, such denoising techniques typically introduce a systematic error, i.e., the denoising bias, which does not decline as rapidly when increasing the sample size, unlike the other type of error, i.e., variance. It can technically lead to slow numerical convergence of the denoising techniques. We propose a new combination framework built upon the James-Stein (JS) estimator, which merges a pair of unbiased and biased rendering images, e.g., a path-traced image and its denoised result. Unlike existing post-correction techniques for image denoising, our framework helps an input denoiser have lower errors than its unbiased input without relying on accurate estimation of per-pixel denoising errors. We demonstrate that our framework based on the well-established JS theories allows us to improve the error reduction rates of state-of-the-art learning-based denoisers more robustly than recent post-denoisers.

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Zheng, Shaokun, Fengshi Zheng, Kun Xu, and Ling-Qi Yan. "Ensemble denoising for Monte Carlo renderings." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–17. http://dx.doi.org/10.1145/3478513.3480510.

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Various denoising methods have been proposed to clean up the noise in Monte Carlo (MC) renderings, each having different advantages, disadvantages, and applicable scenarios. In this paper, we present Ensemble Denoising , an optimization-based technique that combines multiple individual MC denoisers. The combined image is modeled as a per-pixel weighted sum of output images from the individual denoisers. Computation of the optimal weights is formulated as a constrained quadratic programming problem, where we apply a dual-buffer strategy to estimate the overall MSE. We further propose an iterative solver to overcome practical issues involved in the optimization. Besides nice theoretical properties, our ensemble denoiser is demonstrated to be effective and robust, and outperforms any individual denoiser across dozens of scenes and different levels of sample rates. We also perform a comprehensive analysis on the selection of individual denoisers to be combined, providing important and practical guides for users.

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Hofmann, Nikolai, Jon Hasselgren, and Jacob Munkberg. "Joint Neural Denoising of Surfaces and Volumes." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 1 (May 12, 2023): 1–16. http://dx.doi.org/10.1145/3585497.

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Denoisers designed for surface geometry rely on noise-free feature guides for high quality results. However, these guides are not readily available for volumes. Our method enables combined volume and surface denoising in real time from low sample count (4 spp) renderings. The rendered image is decomposed into volume and surface layers, leveraging spatio-temporal neural denoisers for both components. The individual signals are composited using learned weights and denoised transmittance. Our architecture outperforms current denoisers in scenes containing both surfaces and volumes, and produces temporally stable results at interactive rates.

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Han, Kyu Beom, Olivia G. Odenthal, Woo Jae Kim, and Sung-Eui Yoon. "Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising." Proceedings of the ACM on Computer Graphics and Interactive Techniques 6, no. 1 (May 12, 2023): 1–19. http://dx.doi.org/10.1145/3585505.

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Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent approaches implicitly learn to exploit auxiliary features for denoising, which could lead to insufficient utilization of each type of auxiliary features. To overcome such an issue, we propose a denoising framework that relies on an explicit pixel-wise guidance for utilizing auxiliary features. First, we train two denoisers, each trained by a different auxiliary feature (i.e., G-buffers or P-buffers). Then we design our ensembling network to obtain per-pixel ensembling weight maps, which represent pixel-wise guidance for which auxiliary feature should be dominant at reconstructing each individual pixel and use them to ensemble the two denoised results of our denosiers. We also propagate our pixel-wise guidance to the denoisers by jointly training the denoisers and the ensembling network, further guiding the denoisers to focus on regions where G-buffers or P-buffers are relatively important for denoising. Our result and show considerable improvement in denoising performance compared to the baseline denoising model using both G-buffers and P-buffers. The source code is available at https://github.com/qbhan/GuidanceMCDenoising.

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Liu, Shuaiqi, Tong Liu, Lele Gao, Hailiang Li, Qi Hu, Jie Zhao, and Chong Wang. "Convolutional Neural Network and Guided Filtering for SAR Image Denoising." Remote Sensing 11, no. 6 (March 23, 2019): 702. http://dx.doi.org/10.3390/rs11060702.

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Coherent noise often interferes with synthetic aperture radar (SAR), which has a huge impact on subsequent processing and analysis. This paper puts forward a novel algorithm involving the convolutional neural network (CNN) and guided filtering for SAR image denoising, which combines the advantages of model-based optimization and discriminant learning and considers how to obtain the best image information and improve the resolution of the images. The advantages of proposed method are that, firstly, an SAR image is filtered via five different level denoisers to obtain five denoised images, in which the efficient and effective CNN denoiser prior is employed. Later, a guided filtering-based fusion algorithm is used to integrate the five denoised images into a final denoised image. The experimental results indicate that the algorithm cannot eliminate noise, but it does improve the visual effect of the image significantly, allowing it to outperform some recent denoising methods in this field.

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Choi, Joon Hee, Omar A. Elgendy, and Stanley H. Chan. "Optimal Combination of Image Denoisers." IEEE Transactions on Image Processing 28, no. 8 (August 2019): 4016–31. http://dx.doi.org/10.1109/tip.2019.2903321.

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Meng, Xiyan, and Fang Zhuang. "A New Boosting Algorithm for Shrinkage Curve Learning." Mathematical Problems in Engineering 2022 (April 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/6339758.

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To a large extent, classical boosting denoising algorithms can improve denoising performance. However, these algorithms can only work well when the denoisers are linear. In this paper, we propose a boosting algorithm that can be used for a nonlinear denoiser. We further implement the proposed algorithm into a shrinkage curve learning denoising algorithm, which is a nonlinear denoiser. Concurrently, the convergence of the proposed algorithm is proved. Experimental results indicate that the proposed algorithm is effective and the dependence of the shrinkage curve learning denoising algorithm on training samples has improved. In addition, the proposed algorithm can achieve better performance in terms of visual quality and peak signal-to-noise ratio (PSNR).

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Liu, Yukun, Bowen Wan, Daming Shi, and Xiaochun Cheng. "Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution." Remote Sensing 15, no. 2 (January 6, 2023): 364. http://dx.doi.org/10.3390/rs15020364.

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With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.

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Galande, Ashwini S., Vikas Thapa, Hanu Phani Ram Gurram, and Renu John. "Untrained deep network powered with explicit denoiser for phase recovery in inline holography." Applied Physics Letters 122, no. 13 (March 27, 2023): 133701. http://dx.doi.org/10.1063/5.0144795.

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Single-shot reconstruction of the inline hologram is highly desirable as a cost-effective and portable imaging modality in resource-constrained environments. However, the twin image artifacts, caused by the propagation of the conjugated wavefront with missing phase information, contaminate the reconstruction. Existing end-to-end deep learning-based methods require massive training data pairs with environmental and system stability, which is very difficult to achieve. Recently proposed deep image prior (DIP) integrates the physical model of hologram formation into deep neural networks without any prior training requirement. However, the process of fitting the model output to a single measured hologram results in the fitting of interference-related noise. To overcome this problem, we have implemented an untrained deep neural network powered with explicit regularization by denoising (RED), which removes twin images and noise in reconstruction. Our work demonstrates the use of alternating directions of multipliers method (ADMM) to combine DIP and RED into a robust single-shot phase recovery process. The use of ADMM, which is based on the variable splitting approach, made it possible to plug and play different denoisers without the need of explicit differentiation. Experimental results show that the sparsity-promoting denoisers give better results over DIP in terms of phase signal-to-noise ratio (SNR). Considering the computational complexities, we conclude that the total variation denoiser is more appropriate for hologram reconstruction.

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Kim, Bong-Hyun, and S. Madhavi. "Method for Quantum Denoisers Using Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (October 6, 2022): 1–7. http://dx.doi.org/10.1155/2022/4885897.

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In many applications of quantum information science, high-dimensional entanglement is needed. Quantum teleportation is used for transferring information from one place to another using Einstein–Podolsk–Rosen pairs (EPR) and two classical bits of communication in a channel. Since we cannot produce multiple copies of an unknown state for amplification, we will generate multiple EPR pairs. However, after the distribution of the EPR pairs, they will have decreased fidelity with the ideal EPR state. So, to maintain the quantum states and maximize the quantification of the entanglement without losing the strength of the states, we propose to denoise the channel for a few types of noise. We created a random noise source and filtered out the irrelevant information without affecting the relevant information encoded in the quantum states. The proposed model is used for successful denoising of GHZ states from spin flips and bit flip errors. Much of the research work is not carried out by using machine-language-based neural networks for noise-reduction in quantum channels. In this paper, we propose a denoiser called quantum denoiser CNQD, which uses a feedforward convolution neural network model. We tuned our model with highly entangled GHZ states with zero phases and phase between [0, ∏] mixed with different kinds of noise. Finally, the proposed model can be used for optimal quantum communication via noisy quantum channels using GHZ states.

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Gavaskar, Ruturaj G., Chirayu D. Athalye, and Kunal N. Chaudhury. "On Plug-and-Play Regularization Using Linear Denoisers." IEEE Transactions on Image Processing 30 (2021): 4802–13. http://dx.doi.org/10.1109/tip.2021.3075092.

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Zhang, Jie, Qiyuan Zhang, Xixuan Zhao, and Jiangming Kan. "Boosting denoisers with reinforcement learning for image restoration." Soft Computing 26, no. 7 (February 20, 2022): 3261–72. http://dx.doi.org/10.1007/s00500-022-06840-3.

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Gavaskar, Ruturaj G., and Kunal N. Chaudhury. "Plug-and-Play ISTA Converges With Kernel Denoisers." IEEE Signal Processing Letters 27 (2020): 610–14. http://dx.doi.org/10.1109/lsp.2020.2986643.

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Zhou, Yuqian, Jianbo Jiao, Haibin Huang, Yang Wang, Jue Wang, Honghui Shi, and Thomas Huang. "When AWGN-Based Denoiser Meets Real Noises." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13074–81. http://dx.doi.org/10.1609/aaai.v34i07.7009.

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Discriminative learning based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.

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Yu, Lijia, Jie Luo, Shaoping Xu, Xiaojun Chen, and Nan Xiao. "An Unsupervised Weight Map Generative Network for Pixel-Level Combination of Image Denoisers." Applied Sciences 12, no. 12 (June 19, 2022): 6227. http://dx.doi.org/10.3390/app12126227.

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Image denoising is a classic but still important issue in image processing as the denoising effect has a significant impact on subsequent image processing results, such as target recognition and edge detection. In the past few decades, various denoising methods have been proposed, such as model-based and learning-based methods, and they have achieved promising results. However, no stand-alone method consistently outperforms the others in different complex imaging situations. Based on the complementary strengths of model-based and learning-based methods, in this study, we design a pixel-level image combination strategy to leverage their respective advantages for the denoised images (referred to as initial denoised images) generated by individual denoisers. The key to this combination strategy is to generate a corresponding weight map of the same size for each initial denoised image. To this end, we introduce an unsupervised weight map generative network that adjusts its parameters to generate a weight map for each initial denoised image under the guidance of our designed loss function. Using the weight maps, we are able to fully utilize the internal and external information of various denoising methods at a finer granularity, ensuring that the final combined image is close to the optimal. To the best of our knowledge, our enhancement method of combining denoised images at the pixel level is the first proposed in the image combination field. Extensive experiments demonstrate that the proposed method shows superior performance, both quantitatively and visually, and stronger generalization. Specifically, in comparison with the stand-alone denoising methods FFDNet and BM3D, our method improves the average peak signal-to-noise ratio (PSNR) by 0.18 dB to 0.83 dB on two benchmarking datasets crossing different noise levels. Its denoising effect is also greater than other competitive stand-alone methods and combination methods, and has surpassed the denoising effect of the second-best method by 0.03 dB to 1.42 dB. It should be noted that since our image combination strategy is generic, the proposed combined strategy can not only be used for image denoising but can also be extended to low-light image enhancement, image deblurring or image super-resolution.

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Joo, Sunghwan, Sungmin Cha, and Taesup Moon. "DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4031–38. http://dx.doi.org/10.1609/aaai.v33i01.33014031.

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We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original NAIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.

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Xu, Xiaojian, Yu Sun, Jiaming Liu, Brendt Wohlberg, and Ulugbek S. Kamilov. "Provable Convergence of Plug-and-Play Priors With MMSE Denoisers." IEEE Signal Processing Letters 27 (2020): 1280–84. http://dx.doi.org/10.1109/lsp.2020.3006390.

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Hait-Fraenkel, Ester, and Guy Gilboa. "Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis." Journal of Visual Communication and Image Representation 75 (February 2021): 103041. http://dx.doi.org/10.1016/j.jvcir.2021.103041.

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Cascarano, Pasquale, Elena Loli Piccolomini, Elena Morotti, and Andrea Sebastiani. "Plug-and-Play gradient-based denoisers applied to CT image enhancement." Applied Mathematics and Computation 422 (June 2022): 126967. http://dx.doi.org/10.1016/j.amc.2022.126967.

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Liu, Yiwen, Shaoping Xu, and Zhenyu Lin. "An Improved Combination of Image Denoisers Using Spatial Local Fusion Strategy." IEEE Access 8 (2020): 150407–21. http://dx.doi.org/10.1109/access.2020.3016766.

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Gross, Dennis, Christoph Schmidl, Nils Jansen, and Guillermo A. Pérez. "Model Checking for Adversarial Multi-Agent Reinforcement Learning with Reactive Defense Methods." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (July 1, 2023): 162–70. http://dx.doi.org/10.1609/icaps.v33i1.27191.

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Cooperative multi-agent reinforcement learning (CMARL) enables agents to achieve a common objective. However, the safety (a.k.a. robustness) of the CMARL agents operating in critical environments is not guaranteed. In particular, agents are susceptible to adversarial noise in their observations that can mislead their decision-making. So-called denoisers aim to remove adversarial noise from observations, yet, they are often error-prone. A key challenge for any rigorous safety verification technique in CMARL settings is the large number of states and transitions, which generally prohibits the construction of a (monolithic) model of the whole system. In this paper, we present a verification method for CMARL agents in settings with or without adversarial attacks or denoisers. Our method relies on a tight integration of CMARL and a verification technique referred to as model checking. We showcase the applicability of our method on various benchmarks from different domains. Our experiments show that our method is indeed suited to verify CMARL agents and that it scales better than a naive approach to model checking.

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Nearing, Jacob T., Gavin M. Douglas, André M. Comeau, and Morgan G. I. Langille. "Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches." PeerJ 6 (August 8, 2018): e5364. http://dx.doi.org/10.7717/peerj.5364.

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High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

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Thomas, Manu Mathew, Gabor Liktor, Christoph Peters, Sungye Kim, Karthik Vaidyanathan, and Angus G. Forbes. "Temporally Stable Real-Time Joint Neural Denoising and Supersampling." Proceedings of the ACM on Computer Graphics and Interactive Techniques 5, no. 3 (July 25, 2022): 1–22. http://dx.doi.org/10.1145/3543870.

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Recent advances in ray tracing hardware bring real-time path tracing into reach, and ray traced soft shadows, glossy reflections, and diffuse global illumination are now common features in games. Nonetheless, ray budgets are still limited. This results in undersampling, which manifests as aliasing and noise. Prior work addresses these issues separately. While temporal supersampling methods based on neural networks have gained a wide use in modern games due to their better robustness, neural denoising remains challenging because of its higher computational cost. We introduce a novel neural network architecture for real-time rendering that combines supersampling and denoising, thus lowering the cost compared to two separate networks. This is achieved by sharing a single low-precision feature extractor with multiple higher-precision filter stages. To reduce cost further, our network takes low-resolution inputs and reconstructs a high-resolution denoised supersampled output. Our technique produces temporally stable high-fidelity results that significantly outperform state-of-the-art real-time statistical or analytical denoisers combined with TAA or neural upsampling to the target resolution.

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Wu, Huixuan, Pan Du, Rohan Kokate, and Jian-Xun Wang. "A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking." PLOS ONE 16, no. 7 (July 9, 2021): e0254051. http://dx.doi.org/10.1371/journal.pone.0254051.

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Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.

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Deledalle, Charles-Alban, Loic Denis, Sonia Tabti, and Florence Tupin. "MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?" IEEE Transactions on Image Processing 26, no. 9 (September 2017): 4389–403. http://dx.doi.org/10.1109/tip.2017.2713946.

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Ahmad, Rizwan, Charles A. Bouman, Gregery T. Buzzard, Stanley Chan, Sizhuo Liu, Edward T. Reehorst, and Philip Schniter. "Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery." IEEE Signal Processing Magazine 37, no. 1 (January 2020): 105–16. http://dx.doi.org/10.1109/msp.2019.2949470.

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Li, Zun, and Jin Wu. "Learning Deep CNN Denoiser Priors for Depth Image Inpainting." Applied Sciences 9, no. 6 (March 15, 2019): 1103. http://dx.doi.org/10.3390/app9061103.

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Due to the rapid development of RGB-D sensors, increasing attention is being paid to depth image applications. Depth images play an important role in computer vision research. In this paper, we address the problem of inpainting for single depth images without corresponding color images as a guide. Within the framework of model-based optimization methods for depth image inpainting, the split Bregman iteration algorithm was used to transform depth image inpainting into the corresponding denoising subproblem. Then, we trained a set of efficient convolutional neural network (CNN) denoisers to solve this subproblem. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with three traditional methods in terms of visual quality and objective metrics.

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Ma, Ruijun, Shuyi Li, Bob Zhang, and Zhengming Li. "Generative Adaptive Convolutions for Real-World Noisy Image Denoising." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1935–43. http://dx.doi.org/10.1609/aaai.v36i2.20088.

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Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors and in-camera signal processing pipelines. This will induce problems of most deep denoisers for the overfitting or degrading performance due to the noise discrepancy between the training and test sets. To remedy this issue, we propose a novel flexible and adaptive denoising network, coined as FADNet. Our FADNet is equipped with a plane dynamic filter module, which generates weight filters with flexibility that can adapt to the specific input and thereby impedes the FADNet from overfitting to the training data. Specifically, we exploit the advantage of the spatial and channel attention, and utilize this to devise a decoupling filter generation scheme. The generated filters are conditioned on the input and collaboratively applied to the decoded features for representation capability enhancement. We additionally introduce the Fourier transform and its inverse to guide the predicted weight filters to adapt to the noisy input with respect to the image contents. Experimental results demonstrate the superior denoising performances of the proposed FADNet versus the state-of-the-art. In contrast to the existing deep denoisers, our FADNet is not only flexible and efficient, but also exhibits a compelling generalization capability, enjoying tremendous potential for practical usage.

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Zhang, Hao, Xiuyan Yang, and Jianwei Ma. "Can learning from natural image denoising be used for seismic data interpolation?" GEOPHYSICS 85, no. 4 (May 7, 2020): WA115—WA136. http://dx.doi.org/10.1190/geo2019-0243.1.

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We have developed an interpolation method based on the denoising convolutional neural network (CNN) for seismic data. It provides a simple and efficient way to break through the problem of the scarcity of geophysical training labels that are often required by deep learning methods. This new method consists of two steps: (1) training a set of CNN denoisers to learn denoising from natural image noisy-clean pairs and (2) integrating the trained CNN denoisers into the project onto convex set (POCS) framework to perform seismic data interpolation. We call it the CNN-POCS method. This method alleviates the demands of seismic data that require shared similar features in the applications of end-to-end deep learning for seismic data interpolation. Additionally, the adopted method is flexible and applicable for different types of missing traces because the missing or down-sampling locations are not involved in the training step; thus, it is of a plug-and-play nature. These indicate the high generalizability of the proposed method and a reduction in the necessity of problem-specific training. The primary results of synthetic and field data show promising interpolation performances of the adopted CNN-POCS method in terms of the signal-to-noise ratio, dealiasing, and weak-feature reconstruction, in comparison with the traditional [Formula: see text]-[Formula: see text] prediction filtering, curvelet transform, and block-matching 3D filtering methods.

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Ding, Yuehao, Hao Wu, and Guowu Yuan. "A two-stage modular blind denoising algorithm based on real scene." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012071. http://dx.doi.org/10.1088/1742-6596/2216/1/012071.

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Abstract The use of deep learning methods has developed rapidly in image denoising. Now deep learning methods have a good improvement in denoising effects compared to traditional methods, but there are also some problems. On the one hand, these denoisers are more used to remove the noise of a specific distribution, while the image noise distribution in real life is not fixed, so it is more difficult and more practical to denoise the real image. On the other hand, more and more complex network structures and deeper and deeper network models seem to have become a necessary condition for better denoising effects, but its improvement is not linear, and deeper networks are likely to be removed. The improvement in noise effect is minimal. Because of this, we designed a relatively simple network structure to remove real image noise, which includes the noise level estimation stage using the channel attention mechanism, and the non-blind noise reduction stage using the micro-branch structure we designed. Experiments show that our method has good visual perception quality compared with other methods on commonly used image denoising data sets.

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Ma, Yanting, Cynthia Rush, and Dror Baron. "Analysis of Approximate Message Passing With Non-Separable Denoisers and Markov Random Field Priors." IEEE Transactions on Information Theory 65, no. 11 (November 2019): 7367–89. http://dx.doi.org/10.1109/tit.2019.2934152.

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Liehr, Sascha, Christopher Borchardt, and Sven Münzenberger. "Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers." Optics Express 28, no. 26 (December 14, 2020): 39311. http://dx.doi.org/10.1364/oe.402789.

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Kim, Kwanyoung, Shakarim Soltanayev, and Se Young Chun. "Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth." IEEE Journal of Selected Topics in Signal Processing 14, no. 6 (October 2020): 1112–25. http://dx.doi.org/10.1109/jstsp.2020.3007326.

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Deng, Xi, Miloš Hašan, Nathan Carr, Zexiang Xu, and Steve Marschner. "Path graphs." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–15. http://dx.doi.org/10.1145/3478513.3480547.

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To render higher quality images from the samples generated by path tracing with a low sample count, we propose a novel path reuse approach that processes a fixed collection of paths to iteratively refine and improve radiance estimates throughout the scene. Our method operates on a path graph consisting of the union of the traced paths with additional neighbor edges inserted among clustered nearby vertices. Our approach refines the initial noisy radiance estimates via an aggregation operator, treating vertices within clusters as independent sampling techniques that can be combined using MIS. In a novel step, we also introduce a propagation operator to forward the refined estimates along the paths to successive bounces. We apply the aggregation and propagation operations to the graph iteratively, progressively refining the radiance values, converging to fixed-point radiance estimates with lower variance than the original ones. We also introduce a decorrelation (final gather) step, which uses information already in the graph and is cheap to compute, allowing us to combine the method with standard denoisers. Our approach is lightweight, in the sense that it can be easily plugged into any standard path tracer and neural final image denoiser. Furthermore, it is independent of scene complexity, as the graph size only depends on image resolution and average path depth. We demonstrate that our technique leads to realistic rendering results starting from as low as 1 path per pixel, even in complex indoor scenes dominated by multi-bounce indirect illumination.

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de Santi, Natalí S. M., and L. Raul Abramo. "Improving cosmological covariance matrices with machine learning." Journal of Cosmology and Astroparticle Physics 2022, no. 09 (September 1, 2022): 013. http://dx.doi.org/10.1088/1475-7516/2022/09/013.

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Abstract Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise covariance matrices we need huge numbers of observations, or rather costly simulations - neither of which may be viable. In this work we propose a machine learning approach to alleviate this problem in the context of the covariance matrices used in the study of large-scale structure. With only a small amount of data (matrices built with samples of 50-200 halo power spectra) we are able to provide significantly improved covariance matrices, which are almost indistinguishable from the ones built from much larger samples (thousands of spectra). In order to perform this task we trained convolutional neural networks to denoise the covariance matrices, using in the training process a data set made up entirely of spectra extracted from simple, inexpensive halo simulations (mocks). We then show that the method not only removes the noise in the covariance matrices of the cheap simulation, but it is also able to successfully denoise the covariance matrices of halo power spectra from N-body simulations. We compare the denoised matrices with the noisy sample covariance matrices using several metrics, and in all of them the denoised matrices score significantly better, without any signs of spurious artifacts. With the help of the Wishart distribution we show that the end product of the denoiser can be compared with an effective sample augmentation in the input matrices. Finally, we show that, by using the denoised covariance matrices, the cosmological parameters can be recovered with nearly the same accuracy as when using covariance matrices built with a sample of 30,000 spectra in the case of the cheap simulations, and with 15,000 spectra in the case of the N-body simulations. Of particular interest is the bias in the Hubble parameter H 0, which was significantly reduced after applying the denoiser.

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He, Yilin, Yunhua Yao, Yu He, Zhengqi Huang, Pengpeng Ding, Dalong Qi, Zhiyong Wang, Tianqing Jia, Zhenrong Sun, and Shian Zhang. "High-speed compressive wide-field fluorescence microscopy with an alternant deep denoisers-based image reconstruction algorithm." Optics and Lasers in Engineering 165 (June 2023): 107541. http://dx.doi.org/10.1016/j.optlaseng.2023.107541.

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Ben, Guangli, Xifeng Zheng, Yongcheng Wang, Ning Zhang, and Xin Zhang. "A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal." Applied Sciences 11, no. 2 (January 12, 2021): 673. http://dx.doi.org/10.3390/app11020673.

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A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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Lin, Huangxing, Yihong Zhuang, Xinghao Ding, Delu Zeng, Yue Huang, Xiaotong Tu, and John Paisley. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.

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We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as an Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We apply IDDP to various denoising tasks using only observed corrupted data and show that it achieves better denoising results than other self-supervised denoising methods.

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Zhuang, Lina, Michael K. Ng, and Xiyou Fu. "Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior." Remote Sensing 13, no. 20 (October 13, 2021): 4098. http://dx.doi.org/10.3390/rs13204098.

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The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

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Zhuang, Lina, Michael K. Ng, and Xiyou Fu. "Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior." Remote Sensing 13, no. 20 (October 13, 2021): 4098. http://dx.doi.org/10.3390/rs13204098.

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The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.

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Huang, Zhenghua, Zifan Zhu, Yaozong Zhang, Zhicheng Wang, Biyun Xu, Jun Liu, Shaoyi Li, and Hao Fang. "MD3: Model-Driven Deep Remotely Sensed Image Denoising." Remote Sensing 15, no. 2 (January 11, 2023): 445. http://dx.doi.org/10.3390/rs15020445.

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Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.

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Dabbech, A., M. Terris, A. Jackson, M. Ramatsoku, O. M. Smirnov, and Y. Wiaux. "First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006." Astrophysical Journal Letters 939, no. 1 (October 26, 2022): L4. http://dx.doi.org/10.3847/2041-8213/ac98af.

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Abstract We introduce the first AI-based framework for deep, super-resolution, wide-field radio interferometric imaging and demonstrate it on observations of the ESO 137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent “plug-and-play” scheme whereby a denoising operator is injected as an image regularizer in an optimization algorithm, which alternates until convergence between denoising steps and gradient-descent data fidelity steps. We investigate handcrafted and learned variants of high-resolution, high dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO 137-006 from 19 GB of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.

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Zhao, Shengrong, and Hu Liang. "Multi-frame super resolution via deep plug-and-play CNN regularization." Journal of Inverse and Ill-posed Problems 28, no. 4 (August 1, 2020): 533–55. http://dx.doi.org/10.1515/jiip-2019-0054.

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AbstractBecause of the ill-posedness of multi-frame super resolution (MSR), the regularization method plays an important role in the MSR field. Various regularization terms have been proposed to constrain the image to be estimated. However, artifacts also exist in the estimated image due to the artificial tendency in the manually designed prior model. To solve this problem, we propose a novel regularization-based MSR method with learned prior knowledge. By using the variable splitting technique, the fidelity term and regularization term are separated. The fidelity term is associated with an “{L^{2}}-{L^{2}}” form sub-problem. Meanwhile, the sub-problem respect to regularization term is a denoising problem, which can be solved by denoisers learned from a deep convolutional neural network. Different from the traditional regularization methods which employ hand-crafted image priors, in this paper the image prior model is replaced by learned prior implicitly. The two sub-problems are solved alternately and iteratively. The proposed method cannot only handle complex degradation model, but also use the learned prior knowledge to guide the reconstruction process to avoid the artifacts. Both the quantitative and qualitative results demonstrate that the proposed method gains better quality than the state-of-the-art methods.

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Mc Grath, Orlaith, Mohammad W. Sarfraz, Abha Gupta, Yan Yang, and Tariq Aslam. "Clinical Utility of Artificial Intelligence Algorithms to Enhance Wide-Field Optical Coherence Tomography Angiography Images." Journal of Imaging 7, no. 2 (February 10, 2021): 32. http://dx.doi.org/10.3390/jimaging7020032.

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The aim of this paper is to investigate the clinical utility of the application of deep learning denoise algorithms on standard wide-field Optical Coherence Tomography Angiography (OCT-A) images. This was a retrospective case-series assessing forty-nine 10 × 10 mm OCT-A1 macula scans of 49 consecutive patients attending a medical retina clinic over a 6-month period. Thirty-seven patients had pathology; 13 had none. Retinal vascular layers were categorised into superficial or deep capillary plexus. For each category, the retinal experts compared the original standard image with the same image that had intelligent denoise applied. When analysing the Superficial Capillary Plexus (SCP), the denoised image was selected as “best for clinical assessment” in 98% of comparisons. No difference was established in the remaining 2%. On evaluating the Deep Capillary Plexus (DCP), the denoised image was preferred in 35% of comparisons. No difference was found in 65%. There was no evidence of new artefactual features nor loss of anatomical detail in denoised compared to the standard images. The wide-field denoise feature of the Canon Xephilio OCT-A1 produced scans that were clinically preferable over their original OCT-A images, especially for SCP assessment, without evidence for causing a new artefactual error.

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Zhao, Zitian, Wenhan Zhan, Yamin Cheng, Hancong Duan, Yue Wu, and Ke Zhang. "Denoising by Decorated Noise: An Interpretability-Based Framework for Adversarial Example Detection." Wireless Communications and Mobile Computing 2023 (April 11, 2023): 1–11. http://dx.doi.org/10.1155/2023/7669696.

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The intelligent imaging sensors in IoT benefit a lot from the continuous renewal of deep neural networks (DNNs). However, the appearance of adversarial examples leads to skepticism about the trustworthiness of DNNs. Malicious perturbations, even unperceivable for humans, lead to incapacitations of a DNN, bringing about the security problem in the information integration of an IoT system. Adversarial example detection is an intuitive solution to judge if an input is malicious before acceptance. However, the existing detection approaches, more or less, have some shortcomings like (1) modifying the network structure, (2) extra training before deployment, and (3) requiring some prior knowledge about attacks. To address these problems, this paper proposes a novel framework to filter out the adversarial perturbations by superimposing the original images with the noises decorated by a new gradient-independent visualization method, namely, score class activation map (Score-CAM). We propose to trim the Gaussian noises in a way with more explicit semantic meaning and stronger explainability, which is different from the previous studies based on intuitive hypotheses or artificial denoisers. Our framework requires no extra training and gradient calculation, which is friendly to embedded devices with only inference capabilities. Extensive experiments demonstrate that the proposed framework is sufficiently general to detect a wide range of attacks and apply it to different models.

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Oh, Geunwoo, Jonghee Back, Jae-Pil Heo, and Bochang Moon. "Robust Image Denoising of No-Flash Images Guided by Consistent Flash Images." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 1993–2001. http://dx.doi.org/10.1609/aaai.v37i2.25291.

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Images taken in low light conditions typically contain distracting noise, and eliminating such noise is a crucial computer vision problem. Additional photos captured with a camera flash can guide an image denoiser to preserve edges since the flash images often contain fine details with reduced noise. Nonetheless, a denoiser can be misled by inconsistent flash images, which have image structures (e.g., edges) that do not exist in no-flash images. Unfortunately, this disparity frequently occurs as the flash/no-flash pairs are taken in different light conditions. We propose a learning-based technique that robustly fuses the image pairs while considering their inconsistency. Our framework infers consistent flash image patches locally, which have similar image structures with the ground truth, and denoises no-flash images using the inferred ones via a combination model. We demonstrate that our technique can produce more robust results than state-of-the-art methods, given various flash/no-flash pairs with inconsistent image structures. The source code is available at https://github.com/CGLab-GIST/RIDFnF.

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Sang, De Yi, Jian Jun Zhao, and Li Bin Yang. "Denoising Method for Calibration Data of Landing Guidance Radar Based on EMD and Wavelet." Advanced Materials Research 962-965 (June 2014): 2856–62. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2856.

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The noise resulted in the calibration process of the landing guidance radar can cause serious accidents. Analyse the principle of the EMD and wavelet denoising method. Points out the deficiencies of pure EMD or pure wavelet denoising method. Propose a denoising method based on EMD and wavelet. Improved the discriminanting method for high or low frequency components and the discriminanting method for wavelet thresholding. First EMD the signal, then denoise the high frequency components by wavelet, finally, combined the low frequency components and the denoised high frequency components to get the denoised data.

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Khan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman, and Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising." Sensors 21, no. 9 (April 24, 2021): 2998. http://dx.doi.org/10.3390/s21092998.

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Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

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Zou, XiuFang, Dingju Zhu, Jun Huang, Wei Lu, Xinchu Yao, and Zhaotong Lian. "WGAN-Based Image Denoising Algorithm." Journal of Global Information Management 30, no. 9 (January 2022): 1–20. http://dx.doi.org/10.4018/jgim.300821.

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Traditional image denoising algorithms are generally based on spatial domains or transform domains to denoise and smooth the image. The denoised images are not exhaustive, and the depth-of-learning algorithm has better denoising effect and performs well while retaining the original image texture details such as edge characters. In order to enhance denoising capability of images by the restoration of texture details and noise reduction, this article proposes a network model based on the Wasserstein GAN. In the generator, small convolution size is used to extract image features with noise. The extracted image features are denoised, fused and reconstructed into denoised images. A new residual network is proposed to improve the noise removal effect. In the confrontation training, different loss functions are proposed in this paper.

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Su, Yunhao, Caiwen Ma, Junfeng Han, Xuan Wang, Yuanyuan Wang, and Zhou Ji. "Research on Magnetohydrodynamic Angular Rate Sensor Denoising for a Space Laser Stabilization Control System." Applied Sciences 13, no. 10 (May 10, 2023): 5895. http://dx.doi.org/10.3390/app13105895.

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The magnetohydrodynamic angular rate sensor (MHD ARS) is a high-bandwidth, high-accuracy sensor that is increasingly used to measure spacecraft harmonic vibration. However, the amplitude of harmonic vibration is usually on the order of microradian to milliradian, and the induced electric potential signal of MHD ARS is only on the order of nanovolt to microvolt, which is easily disturbed by noise. In this paper, an improved method based on autocorrelation with Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Threshold Denoising (WTD) is proposed to denoise the signal of MHD ARS. Firstly, CEEMDAN is used to decompose noisy signals and obtain intrinsic mode functions (IMFs), and autocorrelation is used to determine the relevant modes where the effective signals are located. Then, the improved threshold and thresholding function are used to denoise the relevant modes. Finally, the denoised signal is obtained by combining the denoised relevant modes. In the experiment, noisy MHD ARS signals were recorded in static and dynamic conditions, and the effects of the proposed method and conventional methods were compared. The results of the Allan variance in the static condition and root-mean-square error in the dynamic condition show that the proposed method can effectively overcome the shortcomings of conventional methods and obtain a better denoising effect.

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Journal articles on the topic 'Denoisers'

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