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Full-reference quality assessment of stereoscopic images by learning binocular visual properties

APPLIED OPTICS 2017年第29期56卷 8291-8302页

作者： Ma, Jian An, Ping Shen, Liquan Li, Kai Minist Educ Key Lab Adv Displays & Syst Applicat Shanghai 200072 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200072 Peoples R China

Stereoscopic imaging technology has been growingly prevalent driven by both the entertainment industry and scientific applications in today's world. But objective quality assessment of stereoscopic images is a challenging task. In this paper, we propose a novel stereoscopic image quality assessment (SIQA) method by jointly considering monocular perception and binocular interaction. As the most significant contribution of this study, binocular perceptual properties of simple and complex cells are considered for full-reference (FR) SIQA. Specifically, the proposed scheme first simulates the receptive fields of simple cells (one class of V1 neurons) using a push-pull combination of receptive fields response, which is used to represent a monocular cue. Further, the receptive fields of complex cells (the other class of V1 neurons) are simulated by using binocular energy response and binocular rivalry response, which are used to represent a binocular cue. Subsequently, various quality-aware features are extracted from the response of area V1 by calculating the self-weighted histogram of the local binary pattern on four types of feature maps of similarity measurement that will change in the presence of distortions. Finally, kernel ridge regression is used to simulate a nonlinear relationship between the quality-aware features and objective quality scores. The performance of our method is evaluated over popular stereoscopic image databases and shown to be competitive with the state-of-the-art FR SIQA algorithms. (C) 2017 Optical Society of America

关键词： image processing algorithms image quality image quality assessment Signal processing Three dimensional image processing Three dimensional imaging

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Blind multi-frame deconvolution by tangential iterative projections (TIP)

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OPTICS EXPRESS 2017年第26期25卷 32305-32322页

作者： Wilding, Dean Soloviev, Oleg Pozzi, Paolo Vdovin, Gleb Verhaegen, Michel Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands ITMO Univ Kronverksky 49 St Petersburg 197101 Russia Flexible Opt BV Polakweg 10-11 NL-2288 GG Rijswijk Netherlands

A methodology for retrieving the unknown object distribution and point-spread functions (PSFs) from a set of images acquired in the presence of temporal phase aberrations is presented in this paper. The method works by finding optimal complimentary linear filters for multi-frame deconvolution. The algorithm uses undemanding computational operations and few a priori, making it simple, fast and robust even at low signal-to-noise ratios. Results of numerical simulations and experimental tests are given as empirical proof, alongside comparisons with other algorithms found in the literature. (C) 2017 Optical Society of America

关键词： Blind deconvolution Diffraction limit Discrete Fourier transforms image processing algorithms Imaging systems Phase retrieval

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Real-time batch inspection system for surface defects on circular optical filters

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APPLIED OPTICS 2022年第32期61卷 9634-9645页

作者： Zheng, Jishi Yu, Wenying Ding, Zhigang Kong, Linghua Liu, Shuqi Chen, Qingqiang Fujian Univ Technol Intelligent Transportat Syst Res Ctr Sch Transportat Fuzhou 350000 Peoples R China Fujian Univ Technol Sch Mech Engn Fuzhou 350000 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QJ England Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350000 Peoples R China

Optical filters, one of the essential parts of many optical instruments, are used to select a specific radiation band of optical devices. There are specifications for the surface quality of the optical filter in order to ensure the instru-ment's regular operation. The traditional machine learning techniques for examining the optical filter surface quality mentioned in the current studies primarily rely on the manual extraction of feature data, which restricts their ability to detect optical filter surfaces with multiple defects. In order to solve the problems of low detection efficiency and poor detection accuracy caused by defects too minor and too numerous types of defects, this paper proposes a real-time batch optical filter surface quality inspection method based on deep learning and image processing techniques. The first part proposes an optical filter surface defect detection and identification method for seven typical defects. A deep learning model is trained for defect detection and recognition by constructing a dataset. The second part uses image processing techniques to locate the accurate position of the defect, determine whether the defect is located within the effective aperture, and analyze the critical eigenvalue data of the defect. The experimental results show that the method improves productivity and product quality and reduces the manual workload by 90%. The proposed model and method also compare the results of surface defect detection with the actual measurement data in the field, verifying that the method has good recognition accuracy while improving efficiency.(c) 2022 Optica Publishing Group

关键词： image processing image processing algorithms Imaging systems Light sources Optical devices Optical surfaces

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Brain segmentation, spatial censoring, and averaging techniques for optical functional connectivity imaging in mice

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BIOMEDICAL OPTICS EXPRESS 2019年第11期10卷 5952-5973页

作者： White, Brian R. Padawer-Curry, Jonah A. Cohen, Akiva S. Licht, Daniel J. Yodh, Arjun G. Childrens Hosp Philadelphia Div Pediat Cardiol Dept Pediat 3401 Civ Ctr BlvdPediat Cardiol-8NW Philadelphia PA 19104 USA Childrens Hosp Philadelphia Div Neurol Dept Pediat 3501 Civ Ctr Blvd Philadelphia PA 19104 USA Childrens Hosp Philadelphia Dept Anesthesiol & Crit Care Med Abramson Res Ctr 3615 Civ Ctr BlvdRoom 816-H Philadelphia PA 19104 USA Univ Penn Dept Phys & Astron 3231 Walnut St Philadelphia PA 19104 USA

Resting-state functional connectivity analysis using optical neuroimaging holds the potential to be a powerful bridge between mouse models of disease and clinical neurologic monitoring. However, analysis techniques specific to optical methods are rudimentary, and algorithms from magnetic resonance imaging are not always applicable to optics. We have developed visual processing tools to increase data quality, improve brain segmentation, and average across sessions with better field-of-view. We demonstrate improved performance using resting-state optical intrinsic signal from normal mice. The proposed methods increase the amount of usable data from neuroimaging studies, improve image fidelity, and should be translatable to human optical neuroimaging systems. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Diffuse optical tomography Functional imaging image processing algorithms image quality Imaging techniques Optical imaging

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Synchronized, concurrent optical coherence tomography and videostroboscopy for monitoring vocal fold morphology and kinematics

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BIOMEDICAL OPTICS EXPRESS 2019年第9期10卷 4450-4461页

作者： Maguluri, Gopi Mehta, Daryush Kobler, James Park, Jesung Iftimia, Nicusor Phys Sci Inc 20 New England Business Ctr Andover MA 01810 USA Massachusetts Gen Hosp Ctr Laryngeal Surg & Voice Rehabil Boston MA 02114 USA

Voice disorders affect a large number of adults in the United States, and their clinical evaluation heavily relies on laryngeal videostroboscopy, which captures the medial-lateral and anterior-posterior motion of the vocal folds using stroboscopic sampling. However, videostroboscopy does not provide direct visualization of the superior-inferior movement of the vocal folds, which yields important clinical insight. In this paper, we present a novel technology that complements videostroboscopic findings by adding the ability to image the coronal plane and visualize the superior-inferior movement of the vocal folds. The technology is based on optical coherence tomography, which is combined with videostroboscopy within the same endoscopic probe to provide spatially and temporally co-registered images of the mucosal wave motion, as well as vocal folds subsurface morphology. We demonstrate the capability of the rigid endoscopic probe, in a benchtop setting, to characterize the complex movement and subsurface structure of the aerodynamically driven excised larynx models within the 50 to 200 Hz phonation range. Our preliminary results encourage future development of this technology with the goal of its use for in vivo laryngeal imaging. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image processing image processing algorithms Imaging systems In vivo imaging Optical coherence tomography Optical imaging

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Sea surface clutter suppression method based on time-domain polarization characteristics of sun glint

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OPTICS EXPRESS 2019年第3期27卷 2142-2158页

作者： Liang, Jian-An Wang, Xia He, Si Jin, Wei-Qi Beijing Inst Technol Sch Optoelect Beijing 100081 Peoples R China

A method for suppressing sea surface clutter, based on the characteristics of sun glint, is proposed. The proposed method is built on an infrared polarization radiation model of the dynamic sea surface. Based on the time-domain polarization characteristics of sun glint in a dynamic sea scene, a method for taking linearly polarized images at different analyzer angles over fixed intervals is used to suppress sea clutter by using the minimum operation. Experimental results show that the proposed method can effectively improve the contrast between a target and its background. Following simplification, this method can also provide a streamlined sea clutter suppression method with obvious results. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image processing image processing algorithms image quality Polarimetric imaging Polarized light Three dimensional imaging

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Efficient image-based object detection for floating weed collection with low cost unmanned floating vehicles

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SOFT COMPUTING 2021年第20期25卷 13093-13101页

作者： Sravanthi, Ravi Sarma, A. S., V Jawaharlal Nehru Technol Univ Ananthapuramu Anantapur Andhra Pradesh India PBR VITS Dept ECE Kavali Andhra Pradesh India

Unmanned floating vehicles (UFVs) can play a major role in the removal of invasive floating aquatic weeds in the water bodies. To provide automation to those UFVs, image or video processing techniques are helpful. Waterline detection from the sky-land-water region is the primary step for UFV autonomous navigation. After detecting the water region, object detection in the water region is the secondary step. In this paper, an object detection algorithm in water for low cost UFVs is proposed. Real-time and accurate waterline detection is done by using the K-means algorithm and Fast Marching method and it is compared with the Hough transform. Objects are detected accurately in the water region by a modified gradient-based image processing algorithm. In this algorithm, instead of Sobel Gradient Operator, Finite Difference Central Gradient Operator is used to achieve high accuracy in detecting objects in the water region. Floating green color weeds are also detected from objects using the green color of the weeds, and weed percentage is also calculated. Experiments were done on Own datasets, and OSF datasets containing freely floating plants, small cluster plants, and birds. Raspberry Pi 3 Model-B processor is used to implement this proposed algorithm. These performance metrics results show that the proposed detection approach is effective, more accurate, and suitable to different lake environments.

关键词： Autonomous floating vehicles Video acquisition image processing algorithms Water line detection Object detection Weed percentage calculation

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Analysis of on-sky sodium profile data from Lick Observatory

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APPLIED OPTICS 2010年第3期49卷 394-402页

作者： Thomas, Sandrine J. Gavel, Donald Kibrick, Robert Univ Calif Santa Cruz Lab Adapt Opt Santa Cruz CA 95064 USA

The next generation of adaptive optics will depend on laser guide stars to increase sky coverage. However, there are a few limitations. The thickness of the sodium layer in the mesosphere at 90 km causes spot elongation, which is more severe for large telescopes. Moreover, the outer-edge subaperture of such large telescopes will resolve variations of sodium atom density seen over the thick layer. We quantify these density fluctuations using real data taken at Lick Observatory. We used the 1 m Nickel telescope to image the return flux due to laser-induced fluorescence from a dye laser launched from the nearby 3 m Shane telescope. This view was from the side allowing the resolution of the sodium return as a function of height. We used drift scanning of the 1 m telescope to provide resolution in time. We show qualitative images of the sodium distribution for different nights and quantitatively study the temporal power spectra of those fluctuations. We conclude that the sodium profiles have an average full width half-maximum of 10 km. However, the extent beyond the nominal 10 km thickness is important for accurate wavefront sensing. The variations in the height of the sodium layer occur on short enough time scales that AO systems for 30 m class telescopes will likely need focus updates on time scales shorter than 100 ms. (C) 2010 Optical Society of America

关键词： image processing algorithms Imaging telescopes Large telescopes Laser guide stars Laser induced fluorescence Wave front sensing

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Three dimensional object recognition with photon counting imagery in the presence of noise

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OPTICS EXPRESS 2010年第25期18卷 26450-26460页

作者： DaneshPanah, Mehdi Javidi, Bahram Watson, Edward A. Univ Connecticut Dept Elect & Comp Eng Storrs CT 06269 USA USAF Res Lab Sensors Directorate Wright Patterson AFB OH 45433 USA

Three dimensional (3D) imaging systems have been recently suggested for passive sensing and recognition of objects in photon-starved environments where only a few photons are emitted or reflected from the object. In this paradigm, it is important to make optimal use of limited information carried by photons. We present a statistical framework for 3D passive object recognition in presence of noise. Since in quantum-limited regime, detector dark noise is present, our approach takes into account the effect of noise on information bearing photons. The model is tested when background noise and dark noise sources are present for identifying a target in a 3D scene. It is shown that reliable object recognition is possible in photon-counting domain. The results suggest that with proper translation of physical characteristics of the imaging system into the information processing algorithms, photon-counting imagery can be used for object classification. (C) 2010 Optical Society of America

关键词： image processing algorithms image recognition algorithms Imaging systems Matched filtering Photon counting Three dimensional imaging

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Gray consistency optical flow algorithm based on mask-R-CNN and a spatial filter for velocity calculation

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APPLIED OPTICS 2021年第34期60卷 10600-10609页

作者： Zhao, Donghua Wu, Yicheng Wang, Chenguang Shen, Chong Tang, Jun Liu, Jun Yu, Hua Lu, Zhumao North Univ China Sch Instrument & Elect Minist Educ Key Lab Instrumentat Sci & Dynam Measurement Taiyuan 030051 Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China State Grid Shanxi Elect Power Res Inst Taiyuan 030001 Peoples R China

The optical flow method has been widely used to measure the vehicle velocity by observing the stationary ground with a camera looking-down. However, when there are moving objects on the stationary ground, the interfering optical flow field will be generated, which decreases the velocity measurement accuracy of a vehicle relative to the ground. In order to reduce the effects caused by moving objects, this paper integrates pyramid Lucas-Kanade (LK) algorithm with the gray consistency method to use the information of color images thoroughly. First, a mask region with convolutional neural network (Mask-R-CNN) is used to recognize the objects that have motions relative to the ground, and it covers them with masks to enhance the similarity between pixels and to reduce the impacts of the noisy moving pixels. Then images are decomposed into three channels, red, green, and blue (i.e., R, G, and B), and processed by median filter. Based on the gray consistency method, the optical flow can be obtained by the pyramidLKalgorithm. Finally, the velocity is calculated by the optical flowvalue. The prominent advantages of the proposed algorithm are: (i) increase the velocity measurement accuracy of a vehicle relative to the ground;(ii) use the information of color images acquired with cameras thoroughly and obtain velocity calculation outputs with less fluctuation;(iii) reduce wrong values caused by noises that are from the origin image and introduced by similar color masks. Four experiments are conducted to test the proposed algorithm and results with superior precision and reliability showthe feasibility and effectiveness of the proposed method for the velocity measurement accuracy of a vehicle relative to the ground. (C) 2021 Optical Society of America

关键词： image processing image processing algorithms Neural networks Nonlinear optical signal processing Optical flow Spatial filtering

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