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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data V"

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CNN FOR LICENSE PLATE MOTION DEBLURRING

CNN FOR LICENSE PLATE MOTION DEBLURRING

引用

IEEE International conference on image Processing

作者： Pavel Svoboda Michal Hradis Lukds Marsik Pavel Zemcik Faculty of Information Technology Brno University of Technology Czech Republic

ISBN: (纸本)9781467399623

In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks (CNN) in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.

关键词： Kernel Deconvolution Licenses image reconstruction Surveillance Cameras Optical character recognition software

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AN EXEMPLAR-BASED HIDDEN MARKOv MODEL FRAMEWORK FOR NONLINEAR STATE-SPACE MODELS

AN EXEMPLAR-BASED HIDDEN MARKOV MODEL FRAMEWORK FOR NONLINEA...

引用

European Signal Processing conference

作者： Redouane Lguensat Ronan Fablet Pierre Ailliot Pierre Tandeo Institut Mines-Telecom Telecom Bretagne CNRS UMR 6285 LabSTICC p?le CID Brest France University of Western Brittany CNRS UMR 6205 LMBA Brest France

ISBN: (纸本)9781509018918

In this work we present a data-driven method for the reconstruction of dynamical systems from noisy and incomplete observation sequences. The key idea is to benefit from the availability of representative datasets of trajectories of the system of interest. These datasets provide an implicit representation of the dynamics of this system, in contrast to the explicit knowledge of the dynamical model. This data-driven strategy is of particular interest in a large variety of situations, e.g., modeling uncertainties and inconsistencies, unknown explicit models, computationally demanding models, etc. We address this exemplar-based reconstruction issue using a Hidden Markov Model (HMM) and we illustrate the relevance of the method for missing data interpolation issues in multivariate time series. As such, our contribution opens new research avenues for a variety of application domains to exploit the wealth of archived observation and simulation data, aiming a better analysis and reconstruction of dynamical systems using past and future observation sequences.

关键词： Hidden Markov models Smoothing methods Europe Trajectory data models Kalman filters Hidden Markov models smoothing methods data models trajectory Kalman filters implicit representation DYNAMICAL SYSTEMS state space

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Compressed sensing denoising for segmentation of localization microscopy data

Compressed sensing denoising for segmentation of localizatio...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Katharina P. Aschenbrenner Sebastian Butzek Christian v. Guthier Matthias Krufczik Michael Hausmann Felix Bestvater Jürgen W. Hesser Experimental Radiation Oncology Heidelberg University Mannheim Germany Kirchhoff-Institute for Physics Heidelberg University Heidelberg Germany German Cancer Research Center Heidelberg Germany

ISBN: (纸本)9781509000128

Localization microscopy (LM) allows to acquire pointillistic superresolution images of biological structures on the nanoscale. However, current structure reconstruction and segmentation approaches suffer from either exclusion of small structures or strong dependence on a-priori knowledge. We propose reconstruction methods based on compressed sensing (CS) denoising in combination with the isodata threshold for segmentation. The methods are verified on artificial test data. For the denoising, a Haar dictionary and a KSvD dictionary learning on artificial data are used. Both methods perform significantly better than the reference algorithm, a linear density filter, in terms of root-mean-square deviation from the ground truth. Furthermore, exemplary results on real LM data of irradiated cell nuclei with Heterochromatin labeling make small structures visible that are suppressed by the reference method. CS denoising demonstrates promising results for reconstruction of LM data.

关键词： Dictionaries Noise reduction image reconstruction image segmentation Microscopy image resolution Labeling

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RADIOASTRONOMICAL image reconstruction WITH REGULARIZED LEAST SQUARES

RADIOASTRONOMICAL IMAGE RECONSTRUCTION WITH REGULARIZED LEAS...

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IEEE International conference on Acoustics, Speech and Signal Processing

作者： Shahrzad Naghibzadeh Ahmad Mouri Sardarabadi Alle-Jan van der veen Faculty of EEMCS Delft University of Technology The Netherlands

ISBN: (纸本)9781479999897

image formation using the data from an array of sensors is a familiar problem in many fields such as radio astronomy, biomedical and geodetic imaging. The problem can be formulated as a least squares (LS) estimation problem and becomes ill-posed at high resolutions, i.e. large number of image pixels. In this paper we propose two regularization methods, one based on weighted truncation of the eigenvalue decomposition of the image deconvolution matrix and the other based on the prior knowledge of the "dirty image" using the available data. The methods are evaluated by simulations as well as actual data from a phased-array radio telescope in the Netherlands, the Low Frequency Array Radio Telescope (LOFAR).

关键词： Array signal processing image formation interferometry regularization radio astronomy

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Underwater Multi-Spectral Photometric Stereo reconstruction from a Single RGBD image

Underwater Multi-Spectral Photometric Stereo Reconstruction ...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and conference

作者： Hengchao Jiao Yisong Luo Nan Wang Lin Qi Junyu Dong Hansheng Lei Ocean University of China Qingdao China The University of Texas Rio Grande Valley USA

ISBN: (纸本)9781509024018

Traditional theories and methods in 3D reconstruction were all proposed with implicit assumption of air environment. However, underwater environment is different in many aspects. The absorption and scattering effects caused by the suspended particles in the water attenuate the image signal, which disqualifies the traditional reconstruction algorithms. In this paper, we propose a novel method to reconstruct high-resolution 3D surface of underwater objects by fusing depth and multispectral photometric stereo based on a single RGBD image. First, we use depth sensor to obtain the object's RGB image with depth information. Then, we remove backscatter by fitting a binary quadratic function and apply simple linear iterative clustering (SLIC) Superpixels to segment the RGB image. Based on these superpixels, we use multispectral photometric stereo(PS) to calculate the objects' surface normal. Since initial depth data contains noise and not enough high-frequency detail, surface normal from PS are fused with depth data for high-resolution 3D reconstruction. Preliminary experimental results demonstrate that our method achieves more accurate estimates of surface normal and 3D reconstruction from a single frame.

关键词： Underwater Multi-Spectral RGBD

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SELF-RESTRAINT OBJECT RECOGNITION BY MODEL BASED CNN LEARNING

SELF-RESTRAINT OBJECT RECOGNITION BY MODEL BASED CNN LEARNIN...

引用

IEEE International conference on image Processing

作者： Yida Wang Weihong Deng Beijing University of Posts and Telecommunications School of Information and Communication Engineering

ISBN: (纸本)9781467399623

CNN has shown excellent performance on object recognition based on huge amount of real images. For training with synthetic data rendered from 3D models alone to reduce the workload of collecting real images, we propose a concatenated self-restraint learning structure lead by a triplet and softmax jointed loss function for object recognition. Locally connected auto encoder trained from rendered images with and without background used for object reconstruction against environment variables produces an additional channel automatically concatenated to RGB channels as input of classification network. This structure makes it possible training a softmax classifier directly from CNN based on synthetic data with our rendering strategy. Our structure halves the gap between training based on real photos and 3D model in both PASCAL and imageNet database compared to GoogleNet.

关键词： CNN Auto encoder reconstruction Synthetic images Triplet loss object recognition Reconstructive Surgical Procedures synthetic data COMPOSITE imageS Learning Rendering Training reconstructions Pascal environment variable self-control Real number Functional training Triplets images

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IMAGING THROUGH SCATTERING MEDIA WITH INTENSITY MODULATED INCOHERENT SOURCES

IMAGING THROUGH SCATTERING MEDIA WITH INTENSITY MODULATED IN...

引用

IEEE International conference on image Processing

作者： Yifu Hu Xin Jin Kaiyun Wei Qionghai Dai Shenzhen Key Lab of Broadband Network & Multimedia Graduate School at Shenzhen Tsinghua University

ISBN: (纸本)9781467399623

Imaging through scattering media is a significant challenge in computational imaging. Recently, a breakthrough technique based on speckle scanning was proposed with outstanding imaging performance. However, the dense angular scanning of the incident laser beam leads to a lengthy scanning process. In this paper, we propose a method based on compressive sensing (CS) to accelerate the data acquisition process. A new imaging system is proposed which adopts mutually incoherent laser beams as light sources and the intensity of each beam is modulated to perform CS measurement. High-resolution fluctuation of the total fluorescence can be well reconstructed from CS measurements with the learned dictionary as basis function. Experimental results show that the proposed method can reduce nearly 70 percent of the data acquisition complexity without impairing the imaging quality.

关键词： Computational imaging Imaging through scattering media Compressive sensing image reconstruction Compressed sensing image reconstruction Imaging laser beams Scattering CONTAINMENT SPRAY carbon monosulfide Cesium LIGHT SOURCES THROUGH data Collection Methods

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ReconNet: Non-Iterative reconstruction of images from Compressively Sensed Measurements

ReconNet: Non-Iterative Reconstruction of Images from Compre...

引用

IEEE conference on Computer vision and Pattern Recognition

作者： Kuldeep Kulkarni Suhas Lohit Pavan Turaga Ronan Kerviche Amit Ashok Sch. of Electr. Comput. & Energy Eng. Arizona State Univ. Tempe AZ USA Coll. of Opt. Sci. Univ. of Arizona Tucson AZ USA Sch. of Arts Media & Eng. Arizona State Univ. Tempe AZ USA

ISBN: (纸本)9781467388528

The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS) random measurements. To this end, we propose a novel convolutional neural network (CNN) architecture which takes in CS measurements of an image as input and outputs an intermediate reconstruction. We call this network, ReconNet. The intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the final reconstructed image. On a standard dataset of images we show significant improvements in reconstruction results (both in terms of PSNR and time complexity) over state-of-the-art iterative CS reconstruction algorithms at various measurement rates. Further, through qualitative experiments on real data collected using our block single pixel camera (SPC), we show that our network is highly robust to sensor noise and can recover visually better quality images than competitive algorithms at extremely low sensing rates of 0.1 and 0.04. To demonstrate that our algorithm can recover semantically informative images even at a low measurement rate of 0.01, we present a very robust proof of concept real-time visual tracking application.

关键词： image reconstruction image coding Computer architecture Current measurement Optical variables measurement Imaging Estimation image reconstruction Optical variables measurement Electric current measurement Current measuring Computer Architecture images image coding Imaging competitive algorithms carbon monosulfide CONTAINMENT SPRAY Cesium Peak signal-to-noise ratio

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A phase space approach to imaging from limited data 8

A phase space approach to imaging from limited data

引用

conference on image reconstruction from incomplete data vIII

作者： Testorf, Markus E. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA

ISBN: (纸本)9781628417661

The optical instrument function is used as the basis to develop optical system theory for imaging applications. The detection of optical signals is conveniently described as the overlap integral of the Wigner distribution functions of instrument and optical signal. Based on this framework various optical imaging systems, including plenoptic cameras, phase-retrieval algorithms, and Shack-Hartman sensors are shown to acquire information about a domain in phase-space, with finite extension and finite resolution. It is demonstrated how phase space optics can be used both to analyze imaging systems, as well as for designing methods for image reconstruction.

关键词： Wigner distribution function phase-space optics signal recovery optical imaging phase retrieval wavefront sensing instrument function

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An image reconstruction Model and Hybrid Algorithm for Limited-angle Projection data

An Image Reconstruction Model and Hybrid Algorithm for Limit...

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IEEE International conference on Bioinformatics and Biomedicine - Medical Informatics and Decision Making

作者： Gao Hongxia Luo Yinghao Chen Kewei Ma Ge Wu Lixuan S China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China S China Univ Technol Minist Educ Engn Res Ctr Precis Elect Mfg Equipment Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781467367981

More and more applications in industrial or medical CT have to adopt limited-angle projection data, such as restricted scanning, decreasing radiation dose and so on. Traditional reconstruction methods for incomplete projection data, including analytic reconstruction and iterative reconstruction, can't meet the demand of limited-angle reconstruction. To deal with Tv reconstruction model's imbalance in reconstructing image information of different direction, this paper proposes a reconstruction model and its corresponding hybrid algorithm, which utilize horizontal direction gradient to assist Tv reconstruction in vertical direction. Experiments results demonstrate the proposed method's effectiveness and performance in image reconstruction from limited-angle projection data.

关键词： image reconstruction limited-angle projection data hybrid algorithm

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