Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) cameras capture high-speed videos by compressing multiple video frames into a measurement frame. However, reconstructing video frames from the compressed measurement frame is challenging. The existing state-of-the-art reconstruction algorithms suffer from low reconstruction quality or heavy time consumption, making them not suitable for real-time applications. In this paper, exploiting the powerful learning ability of deep neural networks (DNN), we propose a novel Tensor Fast Iterative Shrinkage-Thresholding Algorithm Net (Tensor FISTA-Net) as a decoder for SCI video cameras. Tensor FISTA-Net not only learns the sparsest representation of the video frames through convolution layers, but also reduces the reconstruction time significantly through tensor calculations. Experimental results on synthetic datasets show that the proposed Tensor FISTA-Net achieves average PSNR improvement of 1.63∼3.89dB over the state-of-the-art algorithms. Moreover, Tensor FISTA-Net takes less than 2 seconds running time and 12MB memory footprint, making it practical for real-time IoT applications.

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Compressive Video Sensing Based on Intra-Inter-Frame Constraints and Genetic Algorithm

10.3233/faia210418 ◽

2021 ◽

Author(s):

Yuchen Yue ◽

Hua Li ◽

Jianhua Luo

Keyword(s):

Genetic Algorithm ◽

High Speed ◽

Reconstruction Algorithms ◽

Video Capture ◽

Video Signals ◽

Video Frames ◽

Practical Engineering ◽

Priori Information ◽

Inter Frame ◽

Reconstruction Model

Establishing structured reconstruction models and efficient reconstruction algorithms according to practical engineering needs is of great concern in the applied research of Compressed Sensing (CS) theory. Targeting problems during high-speed video capture, the paper proposes a set of video CS scheme based on intra-frame and inter-frame constraints and Genetic Algorithm (GA). Firstly, it employs the intra-frame and inter-frame correlation of the video signals as the priori information, creating a video CS reconstruction model on the basis of temporal and spatial similarity constraints. Then it utilizes overcomplete dictionary of Ridgelet to divide the video frames into three structures, smooth, single-oriented, or multijointed. Video frames cluster according to the structure using Affinity Propagation (AP) algorithm, and finally clusters are reconstructed using evolutionary algorithm. It is proved efficient in terms of reconstruction result in the experiment.

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Low Complexity FFT Factorization for CS Reconstruction

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4675.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 438-442

Keyword(s):

Real Time ◽

High Speed ◽

Performance Metrics ◽

Matching Pursuit ◽

Matrix Multiplication ◽

Low Complexity ◽

Orthogonal Matching Pursuit ◽

Reconstruction Algorithms ◽

Wide Range ◽

Computationally Intensive

In this paper is presented a novel area efficient Fast Fourier transform (FFT) for real-time compressive sensing (CS) reconstruction. Among various methodologies used for CS reconstruction algorithms, Greedy-based orthogonal matching pursuit (OMP) approach provides better solution in terms of accurate implementation with complex computations overhead. Several computationally intensive arithmetic operations like complex matrix multiplication are required to formulate correlative vectors making this algorithm highly complex and power consuming hardware implementation. Computational complexity becomes very important especially in complex FFT models to meet different operational standards and system requirements. In general, for real time applications, FFT transforms are required for high speed computations as well as with least possible complexity overhead in order to support wide range of applications. This paper presents an hardware efficient FFT computation technique with twiddle factor normalization for correlation optimization in orthogonal matching pursuit (OMP). Experimental results are provided to validate the performance metrics of the proposed normalization techniques against complexity and energy related issues. The proposed method is verified by FPGA synthesizer, and validated with appropriate currently available comparative analyzes.

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Adaptive Real-Time Method for Anomaly Detection Using Machine Learning

Proceedings ◽

10.3390/proceedings2020054038 ◽

2020 ◽

Vol 54 (1) ◽

pp. 38

Author(s):

David Novoa-Paradela ◽

Óscar Fontenla-Romero ◽

Bertha Guijarro-Berdiñas

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Real Time ◽

State Of The Art ◽

Learning Ability ◽

Convex Hulls ◽

Detection Systems ◽

Anomalous Data ◽

Frequent Use ◽

Normal Class

Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring and the lack of methods capable of learning in real time, this research presents a new method that provides such online adaptability. The method bases its operation on the properties of scaled convex hulls. It begins building a convex hull, using a minimum set of data, that is adapted and subdivided along time to accurately fit the boundary of the normal class data. The model has online learning ability and its execution can be carried out in a distributed and parallel way, all of them interesting advantages when dealing with big datasets. The method has been compared to other state-of-the-art algorithms demonstrating its effectiveness.

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Deep Convolutional Sequence Approach Towards Real-Time Intelligent Optical Scanning

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2021100105 ◽

2021 ◽

Vol 11 (4) ◽

pp. 63-76

Author(s):

Mansi Mahendru ◽

Sanjay Kumar Dubey ◽

Divya Gaur

Keyword(s):

Real Time ◽

High Speed ◽

Scientific Work ◽

Learning Approaches ◽

Optical Scanning ◽

Optical Scanner ◽

Video Frames ◽

Visual Text ◽

Public Datasets ◽

Vision Application

Visual text recognition is the most dynamic computer vision application due to its rising demand in several applications like crime scene detection, assisting blind people, digitizing, book scanning, etc. However, numerous research works were executed on static visuals having organized text and on captured video frames in the past. The key objective of this study is to develop the real-time intelligent optical scanner that will extract every sequence of text from high-speed video, noisy visual input, and offline handwritten script. The scientific work has been carried out with the combination of multiple deep learning approaches, namely EAST, CNN, and Bi-LSTM with CTC. The system is trained and tested on four public datasets (i.e., ICDAR 2015, SVT, Synth-Text, IAM-3.0) and measured on the basis of recall, precision, and f-measure. Based on the challenges, performance has been examined under three different categories, and the outcomes are optimistic and encouraging for future advancement.

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HIGH-SPEED PARALLEL VLSI ARCHITECTURES FOR IMAGE DECORRELATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800149500016x ◽

1995 ◽

Vol 09 (02) ◽

pp. 343-365

Author(s):

TINKU ACHARYA ◽

AMAR MUKHERJEE

Keyword(s):

Image Compression ◽

Real Time ◽

High Speed ◽

State Of The Art ◽

Parallel Architecture ◽

Vlsi Implementation ◽

Compression Rate ◽

Vlsi Architectures ◽

Vlsi Chip ◽

Vlsi Technology

We present a new high speed parallel architecture and its VLSI implementation to design a special purpose hardware for real-time lossless image compression/ decompression using a decorrelation scheme. The proposed architecture can easily be implemented using state-of-the-art VLSI technology. The hardware yields a high compression rate. A prototype 1-micron VLSI chip based on this architectural idea has been designed. The scheme is favourably comparable to the lossless JPEG standard image compression schemes. We also discuss the parallelization issues of the lossless JPEG standard still compression schemes and their difficulties.

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New transform to project axisymmetric deflection fields along arbitrary rays

Measurement Science and Technology ◽

10.1088/1361-6501/ac3f83 ◽

2021 ◽

Author(s):

Timothy Sipkens ◽

S. J. Grauer ◽

Adam M Steinberg ◽

S N Rogak ◽

Patrick Kirchen

Keyword(s):

High Speed ◽

Gas Flow ◽

State Of The Art ◽

Tomographic Reconstruction ◽

Quantitative Information ◽

Target Object ◽

Line Of Sight ◽

Reconstruction Algorithms ◽

Abel Transform ◽

Axisymmetric Deflection

Abstract Axisymmetric tomography is used to extract quantitative information from line-of-sight measurements of gas flow and combustion fields. For instance, background oriented schlieren (BOS) measurements are typically inverted by tomographic reconstruction to estimate the density field of a high-speed or high-temperature flow. Conventional reconstruction algorithms are based on the inverse Abel transform, which assumes that rays are parallel throughout the target object. However, camera rays are not parallel, and this discrepancy can result in significant errors in many practical imaging scenarios. We present a generalization of the Abel transform for use in tomographic reconstruction of light-ray deflections through an axisymmetric target. The new transform models the exact path of camera rays instead of assuming parallel paths, thereby improving the accuracy of estimates. We demonstrate our approach with a simulated BOS scenario in which we reconstruct noisy synthetic deflection data across a range of camera positions. Results are compared to state-of-the-art Abel-based algorithms. Reconstructions computed using the new transform are consistently more stable and accurate than conventional reconstructions.

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Current Issues: Advanced Digital Technology for Supervising Graduate Clinicians

Perspectives on Administration and Supervision ◽

10.1044/aas20.1.9 ◽

2010 ◽

Vol 20 (1) ◽

pp. 9-13 ◽

Cited By ~ 2

Author(s):

Glenn Tellis ◽

Lori Cimino ◽

Jennifer Alberti

Keyword(s):

Real Time ◽

Digital Technology ◽

Clinical Training ◽

State Of The Art ◽

Clinical Skills ◽

Slow Motion ◽

Video Capture ◽

Microsoft Excel ◽

Clinical Supervisors ◽

Training And Supervision

Abstract The purpose of this article is to provide clinical supervisors with information pertaining to state-of-the-art clinic observation technology. We use a novel video-capture technology, the Landro Play Analyzer, to supervise clinical sessions as well as to train students to improve their clinical skills. We can observe four clinical sessions simultaneously from a central observation center. In addition, speech samples can be analyzed in real-time; saved on a CD, DVD, or flash/jump drive; viewed in slow motion; paused; and analyzed with Microsoft Excel. Procedures for applying the technology for clinical training and supervision will be discussed.

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