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Imaging through strong turbulence with a light field approach

OPTICS EXPRESS 2016年第11期24卷 1975-1986页

作者： Wu, Chensheng Ko, Jonathan Davis, Christopher C. Univ Maryland College Pk MD 20742 USA

Under strong turbulence conditions, object's images can be severely distorted and become unrecognizable throughout the observing time. Conventional image restoring algorithms do not perform effectively in these circumstances due to the loss of good references on the object. We propose the use a plenoptic sensor as a light field camera to map a conventional camera image onto a cell image array in the image's sub-angular spaces. Accordingly, each cell image on the plenoptic sensor is equivalent to the image acquired by a sub-aperture of the imaging lens. The wavefront distortion over the lens aperture can be analyzed by comparing cell images in the plenoptic sensor. By using a modified "Laplacian" metric, we can identify a good cell image in a plenoptic image sequence. The good cell image corresponds with the time and sub-aperture area on the imaging lens where wavefront distortion becomes relatively and momentarily "flat". As a result, it will reveal the fundamental truths of the object that would be severely distorted on normal cameras. In this paper, we will introduce the underlying physics principles and mechanisms of our approach and experimentally demonstrate its effectiveness under strong turbulence conditions. In application, our approach can be used to provide a good reference for conventional image restoring approaches under strong turbulence conditions. This approach can also be used as an independent device to perform object recognition tasks through severe turbulence distortions. (C)2016 Optical Society of America

关键词： Blind deconvolution image metrics image processing image quality Imaging systems Plenoptic imaging

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Some Nonlocal Variational Models Along with Their Fast algorithms 13

Some Nonlocal Variational Models Along with Their Fast Algor...

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13th Web Information systems and Applications Conference, WISA 2016

作者： Zheng, Shixiu Pan, Zhengkuan Duan, Jinming Lu, Wenqi Liu, Cunliang Business School of Qingdao University China College of Computer Science and Technology Qingdao University China School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9781509054374

Models based on local operators can't preserve texture information. Nonlocal models can be used for many image processing tasks. A main advantage of nonlocal models over classical PDE-based algorithms is the ability to handle textures and repetitive structures. Some nonlocal models along with their Split Bregman algorithms are proposed for image denoising, image inpainting, and image segmentation in this paper. Finally, numerical experiments are shown to validate the efficiency and effectiveness of the proposed models. © 2016 IEEE.

关键词： image segmentation

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Bayesian Method for image Recovery from Block Compressive Sensing 50

Bayesian Method for Image Recovery from Block Compressive Se...

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50th IEEE Asilomar Conference on Signals, systems, and Computers (ASILOMARSSC)

作者： Wijewardhana, U. L. Codreanu, M. Latva-aho, M. Univ Oulu Ctr Wireless Commun Dept Commun Engn Oulu 90014 Finland

ISBN: (纸本)9781538639542

We consider the problem of recovering an image using block compressed sensing (BCS). Traditional BCS algorithms recovers each image block independently and utilizes post-processing methods for removing the blocking artifacts. In contrast, we propose an image recovery method free of post-processing, where we utilize a lapped transform (LT) for the sparse representation of the image in order to reduce the blocking artifacts. Specifically, we derive an iterative image reconstruction method, where a small number of adjacent measurement blocks are jointly processed for recovering an image block. For this purpose, we propose a novel sparse Bayesian learning (SBL) algorithm.

关键词： Block compressed sensing image processing sparse Bayesian learning Bayesian compressive sensing

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IMU Based Adaptive Blur Removal Approach using image processing for Railway Inspection 23

IMU Based Adaptive Blur Removal Approach using Image Process...

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23rd International Conference on systems, Signals and image processing (IWSSIP)

作者： Santur, Yunus Karakose, Mehmet Aydin, Ilhan Akin, Erhan Firat Univ Dept Comp Engn Elazig Turkey

ISBN: (纸本)9781467395557

Rail transportation systems, which are commonly used in today's world, should be inspected at certain intervals for possible accidents. During the rail inspection, the physical vibration on rail lines causes a blurring effect on the images. Doing deblurring automatically requires information of blurring rates and specifying the parameters accordingly for deblurring. With this purpose, a test equipment which can move on rail lines and a camera system for the detection of blurring and deblurring is integrated with Inertial Measurement Unit (EMU) is promoted in this study. Then, with Attitude and Heading Reference System (AHRS) algorithm, the effect of blurring at the moment of the vibration is examined, point spread function (PSF) value is chosen dynamically and deblurring is achieved. In order to increase the accuracy rates of detection algorithms, a pretreatment method is proposed for detecting the blur effect and removing it.

关键词： Inertial Measurement Units Deblurring Rail Inspection AHRS image processing Anomaly Detect

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3D visual discomfort prediction using low complexity disparity algorithms

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EURASIP JOURNAL ON image AND VIDEO processing 2016年第1期2016卷 1页

作者： Chen, Jianyu Zhou, Jun Sun, Jun Bovik, Alan C. Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai 200240 Peoples R China Univ Texas Austin LIVE Austin TX 78701 USA

algorithms that predict the degree of visual discomfort experienced when viewing stereoscopic 3D (S3D) images usually first execute some form of disparity calculation. Following that, features are extracted on these disparity maps to build discomfort prediction models. These features may include, for example, the maximum disparity, disparity range, disparity energy, and other measures of the disparity distribution. Hence, the accuracy of prediction largely depends on the accuracy of disparity calculation. Unfortunately, computing disparity maps is expensive and difficult and most leading assessment models are based on features drawn from the outputs of high complexity disparity calculation algorithms that deliver high quality disparity maps. There is no consensus on the type of stereo matching algorithm that should be used for this type of model. Towards filling this gap, we study the relative performances of discomfort prediction models that use disparity algorithms having different levels of complexity. We also propose a set of new discomfort predictive features with good performance even when using low complexity disparity algorithms.

关键词： Visual discomfort Low complexity disparity calculation algorithms 3D NSS Uncertainty map

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Video inpainting of complex scenes based on local statistical model 14

Video inpainting of complex scenes based on local statistica...

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image processing: algorithms and systems xiv 2016

作者： Voronin, V.V. Sizyakin, R.A. Marchuk, V.I. Cen, Yigang Galustov, G.G. Egiazarian, K.O. Don State Technical university Dept. of Radio-Electronics Systems Gagarina 1 Rostov on Don Russia Institute of Information Science Beijing Jiaotong University Beijing China Southern Federal University Nekrasovski 44 Taganrog Russia Dept. of Signal Processing Tampere University of Technology Korkeakoulunkatu 1 TampereFI-33720 Finland

This paper describes a framework for temporally consistent video completion. Proposed method allow to remove dynamic objects or restore missing or tainted regions present in a video sequence by utilizing spatial and temporal information from neighboring scenes. The algorithm iteratively performs following operations: achieve frame;update the scene model;update positions of moving objects;finding a set of descriptors that encapsulate the information necessary to reconstruct a frame;replace parts of the frame occupied by the objects marked for remove with use of a 3D patches. In this paper, we extend an image inpainting algorithm based texture and structure reconstruction by incorporating an improved strategy for video. Our algorithm is able to deal with a variety of challenging situations which naturally arise in video inpainting, such as the correct reconstruction of dynamic textures, multiple moving objects and moving background. Experimental comparisons to state-of-the-art video completion methods demonstrate the effectiveness of the proposed approach. © 2016 Society for Imaging Science and Technology.

关键词： image enhancement

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A Hybrid Color image Quantization Algorithm Based on k-Means and Harmony Search algorithms

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APPLIED ARTIFICIAL INTELLIGENCE 2016年第4期30卷 331-351页

作者： Khaled, Asmaa Abdel-Kader, Rehab F. Yasein, Mohamed S. Port Said Univ Fac Engn Dept Elect Engn Port Fouad 42523 Port Said Egypt

Color quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.

关键词： Search algorithms Hybrid systems Quantization (Physics) K-means clustering Computer graphics

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STRUCTURAL CONTROL & HEALTH MONITORING 2016年第9期23卷 1168-1179页

作者： Ellenberg, A. Kontsos, A. Moon, F. Bartoli, I. Drexel Univ Dept Mech Engn & Mech Philadelphia PA 19104 USA Drexel Univ Dept Civil Architectural & Environm Engn Philadelphia PA 19104 USA

Visual inspection procedures remain the primary method of infrastructure assessment throughout the USA, but their shortcomings are numerous. In addition to their widely acknowledged variability and subjectivity, the large scale of civil infrastructure systems presents expensive access and time requirements that constrain the frequency of visual inspections and result in poor temporal resolution, which hampers effective decision-making. To overcome this challenge, the research reported herein aimed to assess the ability of computer algorithms together with imagery collected by unmanned aerial vehicles (UAV) to extract accurate and quantitative information to help inform infrastructure management decisions. Techniques such as homography and lens distortion correction are used in this article in a post-processing framework that allows the use of color images obtained by UAVs for actual damage quantification measurements. The experiments described in this article utilize a UAV with a mounted camera and provide measurements from a representative infrastructure mockup with several simulated damage scenarios. Deformation measurements, change detection (related to structural features and the size of deterioration), and crack pattern identification were all analyzed. The results indicated that the developed post-processing algorithms were able to extract quantitative information from UAV captured imagery. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： damage quantification unmanned aerial vehicles image processing deformation crack detection

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Quantitative Characterization of Radiographic Weld Defect Based on the Ground Truth Radiographs Made on a Stainless Steel Plates 1

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3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Chitra, P. Sathyabama Univ Fac Elect & Elect Madras 600119 Tamil Nadu India

ISBN: (数字)9788132227557

ISBN: (纸本)9788132227557;9788132227533

This paper presents a new approach for quantification of radiographic defects. This approach is based on calculating the size of the pixel using the known image quality indicator present in the radiographic image. This method is first applied on the ground truth realities of different shapes whose size is known in advance. The proposed method is then validated with the defect (porosity) where the defect is quantified accurately. The image processing techniques applied on the radiographic image are contrast enhancement, noise reduction and image segmentation to quantify the defects present in the radiographic image. The image processing algorithms are validated using image quality parameter Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

关键词： Defect quantification image quality indicator image processing Ground truth radiographs

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Fundus image denoising using FPGA hardware architecture

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2016年第1期54卷 1-13页

作者： Fredj, Amira Hadj Ben Abdallah, Mariem Malek, Jihene Azar, Ahmad Taher Monastir Univ Elect & Microelect Lab Monastir Tunisia Benha Univ Fac Comp & Informat Banha Egypt

image processing algorithms, implemented in hardware, have recently emerged as the most viable solution for improving the performance of image processing systems. In this paper, a version of an anisotropic diffusion technique is used to reduce noise from retinal images, namely Speckle Reducing Anisotropic Diffusion ( SRAD). The SRAD filter can improve images corrupted by multiplicative or additive noise, but it has been the most computationally complex and it has not been suitable for software implementation in real-time processing. In this paper, an efficient Field-Programmable Gate Array ( FPGA)-based implementation of the SRAD filter is presented to accelerate the processing time. A comparison of the most used classical suppression filters like Gaussian, Median, Perona and Malik anisotropic diffusion has been carried out. The experimental results reveal a 38x performance improvement over the original MATLAB implementation and a 1.33x performance improvement over the hardware implementation using the Xilinx System Generator tool.

关键词： SRAD filter FPGA field-programmable gate array parallel architecture retinal fundus image

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