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IEEE International Conference on Image processing

作者： Zhenbo Lu Houqiang Li Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Heifei Anhui China

In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.

关键词： Adaptation models Image restoration Mathematical model Approximation methods Bayes methods TV Minimization

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Image Deblocking via Group Sparsity Optimization

Image Deblocking via Group Sparsity Optimization

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IEEE International Symposium on Circuits and Systems

作者： Zhenbo Lu Houqiang Li Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Heifei Anhui China

ISBN: (纸本)9781479983926

Block-wise compressed image often suffers from the blocking artifacts. In this paper, we propose a novel deblocking scheme for compressed image, by combining image's sparse property and its self-similarity together, called group sparsity optimization. Instead of processing each image patch individually, in the proposed scheme, similar patches in one group are required to be well-represented on learned dictionary collaboratively, using group sparsity regularization. The group sparsity not only imposes every patch's representation to be sparse, bus also requires patches' coefficients in the group share the similar pattern. The experiment results on standard test images demonstrate that our scheme can improve the PSNR of the compressed images by an average of 1.25 dB, and outperform state of the art deblocking approaches.

关键词： Image Deblocking Image Restoration JPEG compression

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Disparity-compensated Inter-layer Motion Prediction Using Standardized HEVC Extensions

Disparity-compensated Inter-layer Motion Prediction Using St...

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IEEE International Symposium on Circuits and Systems

作者： Li Chen Miska M. Hannuksela Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Nokia Technologies Tampere Finland

ISBN: (纸本)9781479983926

The scalable and multiview extensions of the High Efficiency Video Coding share the same high-level syntax coding structure. For the scalable extension, the motion field of the inter-layer reference picture is modified through Motion Field Mapping before used for motion vector prediction. However, the motion field of the inter-layer reference picture is used without modification in the multiview extension. In this paper, a disparity-compensated inter-layer motion prediction is proposed for multiview video coding to achieve disparity compensation in inter-layer motion prediction using the scaled reference layer offset. The experimental results show that comparing with the multiview extension anchor, the proposed method achieves an average of 1.1% bitrate reduction.

关键词： Video coding motion vector Scalability

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Exploration of Image Search Results Quality Assessment

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IEEE Transactions on Big Data 2015年第3期1卷 95-108页

作者： Tian, Xinmei Lu, Yijuan Stender, Nate Yang, Linjun Tao, Dacheng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application Systems University of Science and Technology of China Hefei230027 China Department of Computer Science Texas State University San MarcosTX78666 United States Department of Computer Science University of Nebraska-Lincoln LincolnNE68588 United States Microsoft Corporation RedmondWA98052-7329 United States Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

Image retrieval plays an increasingly important role in our daily lives. There are many factors which affect the quality of image search results, including chosen search algorithms, ranking functions, and indexing features. Applying different settings for these factors generates search result lists with varying levels of quality. However, no setting can always perform optimally for all queries. Therefore, given a set of search result lists generated by different settings, it is crucial to automatically determine which result list is the best in order to present it to users. This paper aims to solve this problem and makes four main innovations. First, a preference learning model is proposed to quantitatively study and formulate the best image search result list identification problem. Second, a set of valuable preference learning related features is proposed by exploring the visual characters of returned images. Third, a query-dependent preference learning model is further designed for building a more precise and query-specific model. Fourth, the proposed approach has been tested on a variety of applications including reranking ability assessment, optimal search engine selection, and synonymous query suggestion. Extensive experimental results on three image search datasets demonstrate the effectiveness and promising potential of the proposed method. © 2015 IEEE.

关键词： Image retrieval

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Efficient background picture coding for videos obtained from static cameras

Efficient background picture coding for videos obtained from...

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IEEE Visual Communications and Image processing (VCIP)

作者： Fangdong Chen Li Li Dong Liu Houqiang Li Zhuoyi Lv Haitao Yang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Huawei Technologies Co. Ltd. Shenzhen China

With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes developed for these videos mostly encode the background as the long-term reference (LTR) to further improve the coding efficiency. However, since the bit allocation of the long-term background reference is not intensively studied, the coding efficiency is still unsatisfactory. Based on the stability analysis of the video content, an efficient background picture coding algorithm for videos obtained from static cameras, which is embedded with the basic unit level bit allocation, is proposed in this paper. Experimental results reveal that on top of the default mode in HEVC, our method offers the performance with 10.8% BD-rate reduction on average. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences with negligible increases of computational complexity in both encoder and decoder.

关键词： Encoding Videos Cameras Distortion Stability analysis Video coding Delays

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IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WALL PAINTING IMAGES

IMPROVED CONJUGATE GRADIENT BUNDLE ADJUSTMENT of DUNHUANG WA...

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ISPRS geospatial Week 2017

作者： Hu, K. Huang, X. You, H. Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences No. 19 North 4th Ring Road West Beijing100190 China Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan Hubei430019 China

Bundle adjustment with additional parameters is identified as a critical step for precise orthoimage generation and 3D reconstruction of Dunhuang wall paintings. Due to the introduction of self-calibration parameters and quasi-planar constraints, the structure of coefficient matrix of the reduced normal equation is banded-bordered, making the solving process of bundle adjustment complex. In this paper, Conjugate Gradient Bundle Adjustment (CGBA) method is deduced by calculus of variations. A preconditioning method based on improved incomplete Cholesky factorization is adopt to reduce the condition number of coefficient matrix, as well as to accelerate the iteration rate of CGBA. Both theoretical analysis and experimental results comparison with conventional method indicate that, the proposed method can effectively conquer the ill-conditioned problem of normal equation and improve the calculation efficiency of bundle adjustment with additional parameters considerably, while maintaining the actual accuracy. © Authors 2017.

关键词： Calculus of variations Conjugate gradient bundle adjustment (CGBA) Dunhuang wall painting images Improved incomplete Cholesky factorization Space complexity Time complexity

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Optimization approach for multi-scale segmentation of remotely sensed imagery under k-means clustering guidance

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Cehui Xuebao/Acta geodaetica et Cartographica Sinica 2015年第5期44卷 526-532页

作者： Wang, Huixian Jin, Huijia Wang, Jiaolong Jiang, Wanshou State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China Hebei Provincial Institute of Cartography Shijiazhuang050031 China

In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means clustering are used to process the original images;then the result of k-means clustering is used to guide objects merging procedure, in which Otsu threshold method is used to automatically select the impact factor of k-means clustering;finally we obtain the segmentation results which are applicable to different scale objects. FNEA method is taken for an example and segmentation experiments are done using a simulated image and a real remote sensing image from geoEye-1 satellite, qualitative and quantitative evaluation demonstrates that the proposed method can obtain high quality segmentation results. ©, 2015, SinoMaps Press. All right reserved.

关键词： Remote sensing

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GLOBAL PATCH MATCHING

GLOBAL PATCH MATCHING

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ISPRS geospatial Week 2017

作者： Huang, X. Hu, K. Ling, X. Zhang, Y. Lu, Z. Zhou, G. Wuhan Engineering Science and Technology Institute Jiangda Road 30 Wuhan China Wuhan Sense Tour Tecnhonogy Company Luoshi South Road 320 Wuhan China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing100190 China School of Remote Sensing and Information Engineering Wuhan University China

This paper introduces a novel global patch matching method that focuses on how to remove fronto-parallel bias and obtain continuous smooth surfaces with assuming that the scenes covered by stereos are piecewise continuous. Firstly, simple linear iterative cluster method (SLIC) is used to segment the base image into a series of patches. Then, a global energy function, which consists of a data term and a smoothness term, is built on the patches. The data term is the second-order Taylor expansion of correlation coefficients, and the smoothness term is built by combing connectivity constraints and the coplanarity constraints are combined to construct the smoothness term. Finally, the global energy function can be built by combining the data term and the smoothness term. We rewrite the global energy function in a quadratic matrix function, and use least square methods to obtain the optimal solution. Experiments on Adirondack stereo and Motorcycle stereo of Middlebury benchmark show that the proposed method can remove fronto-parallel bias effectively, and produce continuous smooth surfaces. © Authors 2017.

关键词： 3D Label Algorithm Fronto-parallel Bias Global Optimization Matrix Energy Function Patch Matching

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An extended processing scheme for coherent integration and parameter estimation based on matched filtering in passive radar

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第11期15卷 1071-1085页

作者： Xin GUAN Li-hua ZHONG Dong-hui HU Chi-biao DING Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences University of Chinese Academy of Sciences

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

关键词： keystone transform Matched filtering MDCFT Mismatching Passive radar Velocity estimation

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geometry-aware image completion via multiple examples 22

Geometry-aware image completion via multiple examples

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22nd Pacific Conference on Computer Graphics and Applications, PG 2014

作者： Wang, Guihang Chen, Xuejin Hu, Siyu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China

ISBN: (纸本)9783905674736

When browsing through photographs taken during a trip, it can be a distressing discovery to find many other bystanders captured within the frame. A visually compelling snapshot preserves the desired subject in the foreground, and eliminates irrelevant persons or objects. In this paper, we present a new image completion algorithm that retains the geometric consistency of the scene using Internet photo collections. Our key insight allows us to successfully fill the missing regions with content derived from related images with distinct features, or from visible parts of the input image with repetitive textures. The final composition for the missing regions is guided by a confidence map combining the two types of example images. We validate our algorithm on a variety of scenes which are challenging for state-of-the-art completion techniques. The completion results successfully preserve the geometry consistency for complex structures in a wide range of depths. © The Eurographics Association 2014.

关键词： geometry

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