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Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Wen-Na Zhang Yun-xia Liu Ze-Yu Zou Yun-Li Zang Yang Yang Bonnie Ngai-Fong Law Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China School of Control Science and Engineering Shandong University Jinan China Integrated Electronic Systems Lab Co. Ltd Jinan China School of Information Science and Engineering Shandong University Qingdao China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728132488

ISBN: (纸本)9781728132495

Sensor Pattern Noise (SPN) has proven to be an effective fingerprint for source camera identification, while its estimation accuracy heavily relies on denoising algorithm. In this paper, an effective source camera identification scheme based on Multi-Scale Expected Patch Log Likelihood (MSEPLL) denoising algorithm is proposed, firstly. With enhanced prior modeling across multiple scales, MSEPLL can accurately restore the original image. As a consequence, estimated SPN is less influenced by image content. Secondly, the source camera identification problem is formulated by hypothesis testing, where normalized correlation coefficient is adopted for SPN detection. Finally, the effectiveness of the proposed method is verified by abundant experiments in terms of identification accuracy as well as receiver operating characteristic. Performance improvement is more prominent for small image patches, which is more conducive to real forensics applications.

关键词： Cameras Maximum likelihood estimation Gaussian processes Wiener filters image denoising image restoration

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2018 International conference on Wireless Communications, signal processing and Networking, WiSPNET 2018

2018 International Conference on Wireless Communications, Si...

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3rd IEEE International conference on Wireless Communications, signal processing and Networking, WiSPNET 2018

ISBN: (纸本)9781538636244

The proceedings contain 94 papers. The topics discussed include: stochastic network optimization of data dissemination for multi-hop routing in VANETs;an improved adaptive sparse channel estimation method for next generation wireless broadband;music perception analysis on hearing impaired listeners;dual-band bandpass filter using complementary split ring resonators for x-band applications;multi-modal humanoid robot;fusion methods for hyperspectral image and LIDAR data at pixel-level;and accelerated feature descriptor matching in images using threshold accepting.

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Research on real-time NUC method of infrared TDI detector based on bi-directional scanning-imaging mechanism 5

Research on real-time NUC method of infrared TDI detector ba...

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5th conference on Frontiers in Optical Imaging Technology and applications (FOI)

作者： Ma Feng Yin Guoping Tang Shuai Tianjin Jinhang Inst Tech Phys Tianjin 300308 Peoples R China

ISBN: (数字)9781510622906

ISBN: (纸本)9781510622906

This article proposes a real-time calibrating(non-uniformity correction) method for the bi-directional scanning infrared TDI imaging load on the aerospace platform. The method is completed through efficient cooperation of a scanning mechanism, a real-time calibration device, and an image preprocessing device, by employing the scanning blanking period for bi-directional calibration. The image preprocessing device uses high-performance FPGA as the core processor, which can perform flexible working sequence control of the detector, accurate detection of calibration synchronization signal, fast acquisition of calibration data and efficient pipelining data processing. Moreover, by using the Micro Blaze IP core, the preprocessing device can calculate the calibration coefficients of two-point correction in real time and also fulfill communication with the host computer. Proven by practice, the calibration method of the bi-directional infrared TDI detector described in this paper has high practical value with the advantages of high processing speed, significant effect, and high system integration.

关键词： Bi-directional scanning-imaging Infrared TDI detector Real-time NUC image preprocessing FPGA

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Energy Efficient ApproxSIFT Implementation for image Mosaic with Approximate Computing Technologies 61

Energy Efficient ApproxSIFT Implementation for Image Mosaic ...

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61st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Liu, Zheyu Ren, Erxiang Zhang, Yanming Qiao, Fei Wei, Qi Luo, Li Yang, Huazhong Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781538673928

image mosaic could generate wide-angle images by stitching regional images with their overlapped portions, which is widely used technology in image signal processing. During the procedure, SIFT-feature based approaches are proven to perform best in such methods. However, traditional implementations of SIFT algorithm would consume the most of the processing time and energy. By balancing the processing quality and the design specifications of such applications of image mosaic, approximate computing hardware implementations have been adopted in this work to improve the energy-efficiency with ignorable quality degradations. Moreover, the approximate adders of LOA have been used to replace the exact adder to carry out the Gaussian convolution process of the algorithms, which further boost the SIFT operation performance, so-called ApproxSIFT. Simulation results of logic level show that, compared with the traditional method of exact computing, the ApproxSIFT hardware implementation with approximate adders significantly improve the image mosaic system, which achieves 2.4x and 3.5x in speed and power consumption, respectively.

关键词： image mosaic SIFT Approximate computing Low-Part-OR Adder (LOA)

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Landmark Scanning by Using Infrared Sensor for Simultaneous Localization and Mapping Application 14

Landmark Scanning by Using Infrared Sensor for Simultaneous ...

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IEEE 14th International Colloquium on signal processing and its applications (CSPA)

作者： Norazman, Muhammad Fikri Thamrin, Norashikin M. Univ Teknol Mara Malaysia Fac Elect Engn Shah Alam 40450 Selangor Malaysia

ISBN: (纸本)9781538603895

This paper presents object scanning for landmarks detection based on multiple scanning of a non-stationary and discrete sensor for the autonomous vehicle in unknown environment. To preserve a complete mapping and localization techniques, a massive, expensive and complicated computation as well as algorithms have been applied by numerous researchers. On top of that, the utilized sensors are often costly to provide an accurate landmarks or obstacles observation and detection. Therefore, in this work, a mapping technique based on Simultaneous Localization and Mapping (SLAM) in the unknown environment for the autonomous vehicle platform is designed by implementing a low cost rotational non-stationary discrete sensor to visualize and differentiate the objects or obstacles that is useful for the unmanned vehicle to find the eligible pathways during the navigation. This sensor is mounted on the servo motor so that it can rotate while measuring the distance of the detected object, in x, y and z-planes to perform the multiple scanning operation. The scanned data is collected and then plotted in three-dimensional (3D) graphical image representation by using MATLAB to construct the image of the detected obstacle or landmark in this unknown environment. It is found that, the infrared sensor is more suitable to define the shape of the detected object or landmark than to determine the size of it.

关键词： simultaneous localization and mapping mapping object visualization infrared sensor landmarks scanning

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Pattern-learning-based Noise Elimination Algorithm in Photoacoustic Sensing and Imaging 40

Pattern-learning-based Noise Elimination Algorithm in Photoa...

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40th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Tu, Zhi Wang, Boshi Duan, Tingyang Gao, Fei ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

ISBN: (纸本)9781538636466

As one of the fastest-growing imaging modalities in recent years, photoacoustic (PA) imaging has attracted tremendous research interest for various applications including anatomical, functional and molecular imaging. However, the PA signal's amplitude is usually quite weak and can be easily distorted by instrumental noise and interference, which can severely degrade the image quality. To improve the PA signal's signal-to-noise ratio efficiently, this paper introduces a pattern-learning based PA (PLPA) detection method to eliminate the periodically interference noise for PA sensing and imaging. Both simulation and experimental results are demonstrated to prove the validity of the proposed algorithm.

关键词： Interference Imaging White noise signal processing algorithms Sensors Noise measurement Training

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Noise-residue learning convolutional network model for magnetic resonance image enhancement

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Journal of Physics: conference Series 2021年第1期2089卷

作者： Ram Singh Lakhwinder Kaur Department of Computer Science & Engineering Punjabi University Patiala - 147002 (Punjab) INDIA

Magnetic Resonance image (MRI) is an important medical image acquisition technique used to acquire high contrast images of human body anatomical structures and soft tissue organs. MRI system does not use any harmful radioactive ionized material like x-rays and computerized tomography (CT) imaging techniques. High-resolution MRI is desirable in many clinical applications such as tumor segmentation, image registration, edges & boundary detection, and image classification. During MRI acquisition, many practical constraints limit the MRI quality by introducing random Gaussian noise and some other artifacts by the thermal energy of the patient body, random scanner voltage fluctuations, body motion artifacts, electronics circuits impulse noise, etc. High-resolution MRI can be acquired by increasing scan time, but considering patient comfort, it is not preferred in practice. Hence, postacquisition image processing techniques are used to filter noise contents and enhance the MRI quality to make it fit for further image analysis tasks. The main motive of MRI enhancement is to reconstruct a high-quality MRI while improving and retaining its important features. The new deep learning image denoising and artifacts removal methods have shown tremendous potential for high-quality image reconstruction from noise degraded MRI while preserving useful image information. This paper presents a noise-residue learning convolution neural network (CNN) model to denoise and enhance the quality of noise-corrupted low-resolution MR images. The proposed technique shows better performance in comparison with other conventional MRI enhancement methods. The reconstructed image quality is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics by optimizing information loss in reconstructed MRI measured in mean squared error (MSE) metric.

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Color Distortion Removal for Heart Rate Monitoring in Fitness Scenario

Color Distortion Removal for Heart Rate Monitoring in Fitnes...

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IEEE International conference on Parallel and Distributed Computing, applications and Technologies (PDCAT)

作者： Quoc-Viet Tran Shun-Feng Su Minh-Quang Tran Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Heart rate estimation from fitness plays an important role in the evaluation of fitness exercises. Conventional approaches use the photoplethysmography (PPG) sensor to consider the change of light absorption on the wrist skin for heart rate estimation. However, users are required to buy smartwatches for using this function. Various approaches based on video analysis are recently implemented for surveillance purpose. However, it is unstable for motion scenario such as fitness exercises due to the color distortion induced by movement. POS and CHROM are introduced to address this issue. Since the fixed projection planes from POS and CHROM are given in several sources of light, it is not widely applied for surveillance applications. Therefore, a novel projection plane that is adaptively changed with the lighting environment is proposed to estimate the heart rate from fitness videos in ambient light. Moreover, image and digital signal processing techniques are also applied to extract the clean pulse signal from a novel projection plane. From the experiments conducted, the proposed approach outperformed the existing approaches to be the best model for heart rate estimation from fitness videos with the accuracy up to 91.08%.

关键词： Skin Heart rate Color image color analysis Distortion Estimation Face

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Anomalies detected in hyperspectral images using the Rx-based detector 24

Anomalies detected in hyperspectral images using the RX-base...

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conference on image and signal processing for Remote Sensing xxIV

作者： Wu, Jee-Cheng Wang, Geng-Gui Natl Ilan Univ Dept Civil Engn 1Sect 1Sheng Lung Rd I Lan City 260 Taiwan

ISBN: (纸本)9781510621626

The anomaly detector (AD) is an important topic in the exploitation of hyperspectral image (HSI). Because unsupervised AD can detect the objects whose spectra are different from their surroundings under the conditions of no spectral libraries and atmospheric correction, it becomes increasingly important in hyperspectral military and civilian applications. Although there have been many ADs proposed, the Reed-xialoi detector (RxD) is the most typical one, which has been successfully applied to many practical applications in terms of HSI anomaly detection. However, RxD suffers from "small sample size" problem. This paper focuses on the investigation of the three issues related to anomaly detection in real HSIs. That is, how many pixels of a target will be detected as an anomaly? Then, is an anomaly responded to its proximity? Finally, what kinds of anomalies will be detected? Some well-known Rx-based detectors are programmed, such as global Rx, local Rx, subspace Rx, weight Rx and uniform target detector. And those detectors are applied to three hyperspectral datasets acquired by both the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital imagery Collection Experiment (HYDICE) sensors. Experimental results show that an anomaly pixel is detected, if at least one similar pixel is found at 8-surrounding pixels. The detected anomaly pixels respond to the selected background rather than to spatial neighborhood relationship. Moreover, the Rx-based detectors can't recognize detected anomaly attributes, and no one Rx-based detector performance is statistically superior to the others.

关键词： Anomaly detector generalized likelihood ratio test hyperspectral image receiver operating characteristic

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*** - Blind SAR Data Focusing 24

*** - Blind SAR Data Focusing

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conference on image and signal processing for Remote Sensing xxIV

作者： Guaragnella, Cataldo D'Orazio, Tiziana Politecn Bari DEI Dept Elect & Informat Engn Bari Italy CNR Italian Natl Res Council STIIMA Inst Intelligent Ind Technol & Syst Adv Mf Bari Italy

ISBN: (纸本)9781510621626

Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. The increasing interest of the scientific community to simplify SAR sensors and develop automatic system to quickly obtain a sufficiently good precision image is fostered by the will of developing low-cost/light-weight SAR systems to be carried by drones. Standard SAR raw data processing techniques assume uniform motion of the satellite (or aerial vehicle) and a fixed antenna beam pointing sideway orthogonally to the motion path, assumed rectilinear. In the same hypothesis, a novel blind data focusing technique is presented, able to obtain good quality images of the inspected area, in the presence of a strong point scatterer, without the use of ancillary data information. Despite SAR data processing is a well established imaging technology that has become fundamental in several fields and applications, in this paper a novel approach has been used to exploit coherent illumination, demonstrating the possibility of extracting a large part of the ancillary data information from the raw data itself, to be used in the focusing procedure. Preliminary results - still prone to enhancements - are presented for ERS raw data focusing. The proposed Matlab software is distributed under the Noncommercial - Share Alike 4.0 - International Creative Common license by the authors.

关键词： Drone systems UAV SAR systems on drones efficient focusing of SAR data Inverse problem radar theory remote sensing SAR data focusing phase shifts range migration satellite trajectory spatial resolution synthetic aperture radar Geometry Geophysics Satellites Focusing Ancillary Data Singular Value Decomposition Blind deconvolution Numerical analysis signal processing Parameter estimation Algorithm Imaging Phase estimation Phase compensation Azimuth Computational modeling Computer Vision image resolution Radar imaging Synthetic Aperture

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