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检索条件"主题词=convolutional sparse coding"

共 168 条记录，以下是151-160 订阅

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Joint Holographic Detection and Reconstruction 10th

Joint Holographic Detection and Reconstruction

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Yellin, Florence Bejar, Benjamin Haeffele, Benjamin D. Mathieu, Evelien Pick, Christian Ray, Stuart C. Vidal, Rene Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9783030326920;9783030326913

Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.

关键词： Holography convolutional sparse coding Phase recovery

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convolutional sparse REPRESENTATIONS WITH GRADIENT PENALTIES

CONVOLUTIONAL SPARSE REPRESENTATIONS WITH GRADIENT PENALTIES

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA

ISBN: (纸本)9781538646588

While convolutional sparse representations enjoy a number of useful properties, they have received limited attention for image reconstruction problems. The present paper compares the performance of block-based and convolutional sparse representations in the removal of Gaussian white noise. The usual formulation of the convolutional sparse coding problem is slightly inferior to the block-based representations in this problem, but the performance of the convolutional form can be boosted beyond that of the block-based form by the inclusion of suitable penalties on the gradients of the coefficient maps.

关键词： convolutional sparse Representations convolutional sparse coding Total Variation

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JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT convolutional NEURAL NETWORKS WITH COUPLED sparse PRIORS

JOINT IMAGE SUPER-RESOLUTION VIA RECURRENT CONVOLUTIONAL NEU...

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IEEE International Conference on Image Processing (ICIP)

作者： Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IMEC Kapeldreef 75 B-3001 Leuven Belgium

ISBN: (纸本)9781728163956

Joint image super-resolution (SR) refers to the reconstruction of a high-resolution image from its low-resolution version with the aid of a high-resolution image from another modality. Inspired by the recent success of recurrent neural networks in single image SR, we propose a novel multimodal recurrent convolutional neural network with coupled sparse priors for joint image SR. Our network fuses representations of the two image modalities at input layers using a learned multimodal convolutional sparse coding network. Additional recurrent convolutional stages are performed to further learn the mapping between the input modalities and the desired high-resolution estimate. We apply the proposed network to the tasks of near-infrared image SR and multi-spectral image SR using RGB images as the guidance modality. Experimental results show the superior performance of the proposed multimodal recurrent convolutional network against several state-of-the-art single-modal and multimodal image SR methods.

关键词： Multimodal image super-resolution convolutional sparse coding RNNs multimodal image fusion

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convolutional-sparse-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION 44

CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND IT...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kaneko, Y. Muramatsu, S. Yasuda, H. Hayasaka, K. Otake, Y. Ono, S. Yukawa, M. Niigata Univ Grad Sch Sci & Tech Niigata Japan Niigata Univ Res Inst Nat Hazard & Disaster Recovery Niigata Japan Niigata Univ Fac Sci Niigata Japan Tokyo Inst Tech Inst Innovat Res Tokyo Japan Niigata Univ Fac Engn Niigata Japan Keio Univ Fac Sci & Tech Keio Japan

ISBN: (纸本)9781479981311

This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data-driven method of analysis used to describe a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has the advantage of reflecting the spatial structure of a target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. This estimation problem is reduced to sparsity aware signal restoration with a hard constraint given by the CSC-DMD prediction, where the algorithm is derived by the primal-dual splitting method. A time series set of water surface and bed shape measured through an experimental river setup is used to train and test the system. From the result, the efficacy of the proposed method is verified.

关键词： Extended dynamic mode decomposition convolutional sparse coding Primal-dual splitting NSOLT

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SPATIAL-SPECTRAL convolutional sparse NEURAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING

SPATIAL-SPECTRAL CONVOLUTIONAL SPARSE NEURAL NETWORK FOR HYP...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Xiong, Fengchao Ye, Minchao Zhou, Jun Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

sparse representation (SR) is a widely accepted hyperspectral image (HSI) denoising model. Because of the curse of dimensionality and the desire to better fit the data, the SR models are typically deployed on small and fully overlapping blocks whose results are averaged to produce the global denoised HSI. This "local-global" denoising mechanism ignores the dependencies between blocks, resulting in visual artifacts. This paper describes the underlying clean HSI with a 3D convolutional sparse coding (CSC) model, representing the HSI with a linear combination of few shift-invariant 3D spatial-spectral filters in a global dictionary. Instead of operating on patches, the CSC model sees the clean HSI is generated from a sum of local atoms that appear in a small number of locations throughout the image, naturally retaining the relationship between pixels. Moreover, we unfold the optimization process of the model into a spatial-spectral convolutional sparse neural network which absorbs the interpretation ability of the model while supporting discriminative learning from data. Experimental results on both synthetic and real-world datasets show that our network achieves competitive denoising performances, qualitatively and quantitatively.

关键词： Hyperspectral image denoising model-based deep learning convolutional sparse coding deep unfolding

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SPoD-Net: Fast Recovery of Microscopic Images Using Learned ISTA 11

SPoD-Net: Fast Recovery of Microscopic Images Using Learned ...

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11th Asian Conference on Machine Learning (ACML)

作者： Hara, Satoshi Chen, Weichih Washio, Takashi Wazawa, Tetsuichi Nagai, Takeharu Osaka Univ Osaka Japan Natl Taiwan Univ Taipei Taiwan

Recovering high quality images from microscopic observations is an essential technology in biological imaging. Existing recovery methods require solving an optimization problem by using iterative algorithms, which are computationally expensive and time consuming. The focus of this study is to accelerate the image recovery by using deep neural networks (DNNs). In our approach, we first train a certain type of DNN by using some observations from microscopes, so that it can well approximate the image recovery process. The recovery of a new observation is then computed thorough a single forward propagation in the trained DNN. In this study, we specifically focus on observations obtained by SPoD (Super-resolution by Polarization Demodulation), a recently developed microscopic technique, and accelerate the image recovery for SPoD by using DNNs. To this end, we propose SPoD-Net, a specifically tailored DNN for fast recovery of SPoD images. Unlike general DNNs, SPoD-Net can be parameterized using a small number of parameters, which is helpful in two ways: (i) it can be stored in a small memory, and (ii) it can be trained efficiently. We also propose a method to stabilize the training of SPoD-Net. In the experiments with the real SPoD observations, we confirmed the effectiveness of SPoD-Net over existing recovery methods. Specifically, we observed that SPoD-Net could recover images with more than a hundred times faster than the existing method.

关键词： sparse coding convolutional sparse coding learned iterative soft-thresholding algorithm deep neural network

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Improving FISTA's Speed of Convergence via a Novel Inertial Sequence 27

Improving FISTA's Speed of Convergence via a Novel Inertial ...

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27th European Signal Processing Conference (EUSIPCO)

作者： Rodriguez, Paul Pontificia Univ Catolica Peru Elect Dept Lima Peru

ISBN: (纸本)9789082797039

The FISTA (fast iterative shrinkage-thresholding algorithm) is a well-known and fast (theoretical O(k(-2)) rate of convergence) procedure for solving optimization problems composed by the sum of two convex functions, such that one is smooth (differentiable) and the other is possible nonsmooth. FISTA can be understood as a first order method with one important aspect: it uses a suitable extragradient rule, i.e.: the gradient is evaluated at a linear combination of the past two iterates, whose weights, are usually referred to as the inertial sequence. While problem dependent, it has a direct impact on the FISTA's practical computational performance. In this paper we propose a novel inertial sequence;when compared to well-established alternative choices, in the context of convolutional sparse coding and Wavelet-based inpainting, our proposed inertial sequence can reduce the number of FISTA's global iterations (and thus overall computational time) by 30% similar to 50% to attain the same level of reduction in the cost functional.

关键词： FISTA inertial sequence proximal gradient method convolutional sparse coding Wavelet-based inpainting

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Emotional Intensity Estimation using Thermal Images 46

Emotional Intensity Estimation using Thermal Images

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46th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Rooj, Suparna Antesh, U. Bhattacharya, Shubhobrata Routray, Aurobinda Mandal, Manas K. Indian Inst Technol Adv Technol Dev Ctr Kharagpur W Bengal India Int Inst Informat Technol Elect & Elect Engn Bhubaneswar Odisha India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India Indian Inst Technol Dept Humanities & Social Sci Kharagpur W Bengal India

ISBN: (纸本)9781728154145

Intensity estimation of genuine emotion is a challenge for an inexpressive face or deceiving emotion. Thermal modality is experimentally seen to have the capability to reflect true emotion. However, emotion intensity in thermal images is not studied much due to the lack of an annotated database. In this paper, we propose a thermal domain-specific feature-based approach to estimate the intensity of emotion sequence images. The method uses labeled apex images of all six emotions from the NVIE dataset. For extracting domain-specific features, firstly, linear filters are learned from apex images using convolutional sparse coding. Then we obtain features using those learned filters. Further, a distance-based emotion intensity method is proposed without any knowledge of the actual intensities. For a specific emotion, this is done by finding the distance between features of sequence images and clusters of apex features. Proper clustering is ensured by using a supervised dimensionality reduction method and further verifying using SVM classification. Also, a way is suggested to calculate the error in the absence of the actual intensities. The experimental results on the standard NVIE dataset validate better performance compared to previous methods.

关键词： convolutional sparse coding discriminant correlation analysis domain-specific features emotional intensity emotion sequence images thermal imaging

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Slice-based Approximate Online convolutional Dictionary Learning via Convergent Accelerated Inertial Algorithm 2

Slice-based Approximate Online Convolutional Dictionary Lear...

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2nd International Workshop on Methodologies for Multimedia (Meet4MM)

作者： Zhao, Yanjuan Deng, Zhiyuan Yin, Yi Wang, Jinjia Yanshan Univ Qinhuangdao Hebei Peoples R China Xidian Univ Xian Peoples R China Kunming Shipborne Equipment Res & Test Ctr Kunming Yunnan Peoples R China

ISBN: (纸本)9798400712005

convolutional dictionary learning (CDL) is a widely used technique in computer vision for accurately capturing local features and texture information in signals. However, most existing CDL methods are based on batch processing, which requires computing the entire dataset at once, resulting in a significant memory requirement that limits further development. To address this issue, researchers have turned to online convolutional dictionary learning (OCDL) in recent years. OCDL inputs data as a stream and stores all historical information in a pair of fixed-size history arrays. This approach avoids the problem of increasing memory requirements with the number of samples. However, current OCDL methods encounter two primary issues when handling big datasets or large dictionaries: the high time complexity of updating history arrays or dictionaries and the challenge of selecting hyperparameters linked to the ADMM algorithm. This paper proposes a slice-based approximate OCDL method called SAOCDL to address the aforementioned problems. The algorithm utilizes a sparse approximation model, where historical data samples are approximated as the convolution sum of the best local dictionary of a single sample and the corresponding local sparse codes. The current sample and a pair of fixed-size history arrays that store this approximation are used to update the best local dictionary. The resulting optimization problem is solved in the spatial domain using the proposed convergent inertial proximal-gradient algorithm, which combines dry friction with Hessian-driven damping. Extensive experiments were conducted on various benchmark datasets, and the results demonstrate that the proposed SAOCDL approach is highly competitive in terms of performance, efficiency, and memory with state-of-the-art OCDL and CDL algorithms.

关键词： convolutional sparse coding Online Dictionary Learning Inertial Proximal-gradient Algorithm Local Processing Dry Friction Hessian

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Nonlinear models for neurophysiological time series

Nonlinear models for neurophysiological time series

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作者： Tom Dupre La Tour Universite Paris-Saclay

学位级别：博士

Brain analysis mainly relies on complex recording techniques and on advanced signal processing tools used to interpret these recordings. In neurophysiological time series, as strong neural oscillations are observed in the mammalian brain, the natural processing tools are centered on spectral analysis, Fourier decomposition, and on linear filtering into canonical frequency bands. While this approach has had significant impact in neuroscience, it may give a misleading representation of the signal. Indeed, it is standard to see neuro-scientists consider small subsets of coefficients, implicitly assuming that the signals of interest are narrow-band, which turns out to be too reductive. Multiple warnings have been raised about this fallacy, and about the need of more appropriate methods to represent the signals. More generally, a large number of neuroscientific studies use ad-hoc recipes to analyze time series and describe their properties. Importantly, these methods are heavily based on narrow-band filtering and on custom correlation metrics, and they fail to give a goodness of fit. Therefore, setting the parameters of these methods can only be driven by how much they lead to a strong value of the metric. As a consequence, even though these metrics give reasonable information, a legitimate and controlled comparison of methods and parameters, and therefore of the results, is impossible. This is the case for instance for a phenomenon known as phase-amplitude coupling (PAC), which consists in an amplitude modulation of a high frequency signal, time-locked with a specific phase of a slow neural oscillation. In this work, we first propose to use driven autoregressive models (DAR) on neuro- physiological time-series. These models give a spectral representation of the signal conditionally to another signal, and thus are able to capture PAC in a probabilistic model of the signal, for which statistical inference is fast and well-posed. Giving a proper model to the signal enables

关键词： Time series modeling autoregressive convolutional sparse coding cross-frequency coupling multivariate encoding neurophysiology magneto-encephalography

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