Relevant bibliographies by topics / Weighted sparse reconstruction

Academic literature on the topic 'Weighted sparse reconstruction'

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Published: 10 March 2023

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Journal articles on the topic "Weighted sparse reconstruction"

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Deng, Jun, Guanghui Ren, Yansheng Jin, and Wenjing Ning. "Iterative Weighted Gradient Projection for Sparse Reconstruction." Information Technology Journal 10, no. 7 (June 15, 2011): 1409–14. http://dx.doi.org/10.3923/itj.2011.1409.1414.

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Pei, Liye, Hua Jiang, and Ming Li. "Weighted double‐backtracking matching pursuit for block‐sparse reconstruction." IET Signal Processing 10, no. 8 (October 2016): 930–35. http://dx.doi.org/10.1049/iet-spr.2016.0036.

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Pinchera, Daniele, and Marco Donald Migliore. "Accurate Reconstruction of the Radiation of Sparse Sources from a Small Set of Near-Field Measurements by Means of a Smooth-Weighted Norm for Cluster-Sparsity Problems." Electronics 10, no. 22 (November 19, 2021): 2854. http://dx.doi.org/10.3390/electronics10222854.

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The aim of this contribution is to present an approach that allows to improve the quality of the reconstruction of the far-field from a small number of measured samples by means of sparse recovery using a relatively coarse grid for source positions (with sample spacing of the order of λ/8) compared to the grid usually required. In particular, the iterative method proposed employs a smooth-weighted constrained minimization, that guarantees a better probability of correct estimate of the sparse sources and an improved quality in the reconstruction, with a similar computational effort respect to the standard ℓ1 re-weighted minimization approach.
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Xu, Congcong, Bo Yang, Fupei Guo, Wenfeng Zheng, and Philippe Poignet. "Sparse-view CBCT reconstruction via weighted Schatten p-norm minimization." Optics Express 28, no. 24 (November 9, 2020): 35469. http://dx.doi.org/10.1364/oe.404471.

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Wu, Yapeng, Min Yang, Linfeng He, Qiang Lin, Meimei Wu, Zhengyao Li, Yuqing Li, and Xiaoguang Liu. "Sparse-View Neutron CT Reconstruction Using a Modified Weighted Total Difference Minimization Method." Applied Sciences 11, no. 22 (November 19, 2021): 10942. http://dx.doi.org/10.3390/app112210942.

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Indirect neutron imaging is an effective method for nondestructive testing of spent nuclear fuel elements. Considering the difficulty of obtaining experimental data in a high-radiation environment and the characteristic of high noise of neutron images, it is difficult to use the traditional FBP algorithm to recover the complete information of the sample based on the limited projection data. Therefore, it is necessary to develop the sparse-view CT reconstruction algorithm for indirect neutron imaging. In order to improve the quality of the reconstruction image, an iterative reconstruction method combining SIRT, MRP, and WTDM regularization is proposed. The reconstruction results obtained by using the proposed method on simulated data and actual neutron projection data are compared with the results of four other algorithms (FBP, SIRT, SIRT-TV, and SIRT-WTDM). The experimental results show that the SIRT-MWTDM algorithm has great advantages in both objective evaluation index and subjective observation in the reconstruction image of simulated data and neutron projection data.
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Zhang, Yi, Kang Yang, Yining Zhu, Wenjun Xia, Peng Bao, and Jiliu Zhou. "NOWNUNM: Nonlocal Weighted Nuclear Norm Minimization for Sparse-Sampling CT Reconstruction." IEEE Access 6 (2018): 73370–79. http://dx.doi.org/10.1109/access.2018.2881966.

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Abbasi, Ashkan, and Amirhassan Monadjemi. "Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation." Journal of Biomedical Optics 23, no. 03 (March 24, 2018): 1. http://dx.doi.org/10.1117/1.jbo.23.3.036011.

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Zonoobi, Dornoosh, and Ashraf A. Kassim. "On the reconstruction of sequences of sparse signals – The Weighted-CS." Journal of Visual Communication and Image Representation 24, no. 2 (February 2013): 196–202. http://dx.doi.org/10.1016/j.jvcir.2012.05.002.

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Zheng, Penggen, Huimin Zhao, Jin Zhan, Yijun Yan, Jinchang Ren, Jujian Lv, and Zhihui Huang. "Incremental learning-based visual tracking with weighted discriminative dictionaries." International Journal of Advanced Robotic Systems 16, no. 6 (November 1, 2019): 172988141989015. http://dx.doi.org/10.1177/1729881419890155.

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Existing sparse representation-based visual tracking methods detect the target positions by minimizing the reconstruction error. However, due to complex background, illumination change, and occlusion problems, these methods are difficult to locate the target properly. In this article, we propose a novel visual tracking method based on weighted discriminative dictionaries and a pyramidal feature selection strategy. First, we utilize color features and texture features of the training samples to obtain multiple discriminative dictionaries. Then, we use the position information of those samples to assign weights to the base vectors in dictionaries. For robust visual tracking, we propose a pyramidal sparse feature selection strategy where the weights of base vectors and reconstruction errors in different feature are integrated together to get the best target regions. At the same time, we measure feature reliability to dynamically adjust the weights of different features. In addition, we introduce a scenario-aware mechanism and an incremental dictionary update method based on noise energy analysis. Comparison experiments show that the proposed algorithm outperforms several state-of-the-art methods, and useful quantitative and qualitative analyses are also carried out.
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Benuwa, Ben-Bright. "Virtual Kernel Discriminative Dictionary Learning With Weighted KNN for Video Analysis." International Journal of Data Analytics 3, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijda.297521.

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Recently Kernel-Based Discriminative Dictionary (KDDL) for Video Semantic Content Analysis (VSCA) has become very popular research area, particularly in Human Computer Interactions and Computer Vision decades. Nonetheless, the existing KDDL approaches based on reconstruction error classification, coupled with sparse coefficients do not fully consider discrimination, which is essential for classification performance between video samples, despite their numerous successes. In addition, the size of video samples, an important parameter in kernel-based approaches is mostly ignored. To further improve the accuracy of video semantic classification, a VSC classification approach based on Sparse Coefficient Vector and a Virtual Kernel-based Weighted KNN is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The experimental results show that this method effectively improves recognition and classification accuracy for VSCA compared with some state-of-the-art baseline approaches.
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Dissertations / Theses on the topic "Weighted sparse reconstruction"

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DI, FINA DARIO. "Multi-Target Tracking and Facial Attribute Estimation in Smart Environments." Doctoral thesis, 2016. http://hdl.handle.net/2158/1029030.

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This dissertation presents a study on three different computer vision topics that have applications to smart environments. We first propose a solution to improve multi-target data association based on l1-regularized sparse basis expansions. The method aims to improve the data association process by addressing problems like occlusion and change of appearance. Experimental results show that, for the pure data association problem, our proposed approach achieves state-of-the-art results on standard benchmark datasets. Next, we extend our new data association approach with a novel technique based on a weighted version of sparse reconstruction that enforces long-term consistency in multi-target tracking. We introduce a two-phase approach that first performs local data association, and then periodically uses accumulated usage statistics in order to merge tracklets and enforce long-term, global consistency in tracks. The result is a complete, end-to-end tracking system that is able to reduce tracklet fragmentation and ID switches, and to improve the overall quality of tracking. Finally, we propose a method to jointly estimate face characteristics such as Gender, Age, Ethnicity and head pose. We develop a random forest based method based around a new splitting criterion for multi-objective estimation. Our system achieves results comparable to the state-of-the-art, and has the additional advantage of simultaneously estimating multiple facial characteristics using a single pool of image features rather than characteristic-specific ones.
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Book chapters on the topic "Weighted sparse reconstruction"

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Sun, Shaochao. "Unclear Norm Minimization and Weighted Sparse Reconstruction Cost for Crowd Abnormal Detection." In Communications in Computer and Information Science, 222–29. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3966-9_24.

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Ling, Wang, and Jihao Yin. "The Robust Sparse PCA for Data Reconstructive via Weighted Elastic Net." In Lecture Notes in Electrical Engineering, 225–35. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5803-6_23.

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Harding, Dennis. "Defining issues." In Death and Burial in Iron Age Britain. Oxford University Press, 2015. http://dx.doi.org/10.1093/oso/9780199687565.003.0006.

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The universality of human mortality is the commonest of truisms, but the prospect of mortality evidently has weighed differently on different societies over the course of human history, from the oppressive burden of the later Middle Ages to the more relaxed live-for-the-present-ism of the current generation. The disposal of the dead is at basis a hygienic necessity that is recognized in all but the most socially disrupted circumstances, but the manner of disposal may reveal attitudes of society towards death and the concept of afterlife, or the role of the dead in the continuing life of the community. Even in our contemporary secular society, relatives of the victims of murder or abduction or of death in foreign parts crave the recovery of bodies for due burial, without which they apparently cannot ‘achieve closure’, a condition of grace that might have been considered essential to the dead, but which evidently matters equally to the bereaved. The discipline of archaeology is methodologically disposed to distort the reality of the past in that it seeks to recognize ordered patterns where in reality diversity and apparent irrationality must have been inherent. The keystone of Childe’s approach, the identification of archaeological cultures, was dependent upon recurrence of diagnostic types in association, which would permit the comparison of one cultural assemblage with another in time or space. Even in processual and post-processual approaches the essence is to reduce the ever-burgeoning data-base to some semblance of order, without which it is impossible for interpretation to proceed, other than intuitively, empathically, or experientially, that is, based upon imaginative reconstruction rather than being inferred, however inadequately, from archaeological data. The consequence of this process of classification has been to emphasize certain outstanding classes of data, like long barrows, stone circles, or hillforts, as typical of their period or region, at the expense of a subtler analysis of the many possible variations of settlement or burial sites that are detectable, even from the surviving archaeological record. In recent years there has been a significant shift in archaeological approaches to burial data.
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Conference papers on the topic "Weighted sparse reconstruction"

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Loffeld, Otmar, Thomas Espeter, and Miguel Heredia Conde. "From weighted least squares estimation to sparse CS reconstruction." In 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa). IEEE, 2015. http://dx.doi.org/10.1109/cosera.2015.7330282.

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Yang, Kang, Wenjun Xia, Peng Bao, Jiliu Zhou, and Yi Zhang. "Nonlocal Weighted Nuclear Norm Minimization based Sparse-Sampling CT Image Reconstruction." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759372.

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Liu, Xiaodan, and Xueru Bai. "Sparse Time-Frequency Reconstruction of Weighted ADMM Based on Single-Window." In 2021 CIE International Conference on Radar (Radar). IEEE, 2021. http://dx.doi.org/10.1109/radar53847.2021.10028115.

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Kopsinis, Yannis, Konstantinos Slavakis, Sergios Theodoridis, and Steve McLaughlin. "Reduced complexity online sparse signal reconstruction using projections onto weighted ℓ1 balls." In 2011 17th International Conference on Digital Signal Processing (DSP). IEEE, 2011. http://dx.doi.org/10.1109/icdsp.2011.6005005.

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Carrillo, Rafael E., and Kenneth E. Barner. "Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements." In 2009 43rd Annual Conference on Information Sciences and Systems (CISS). IEEE, 2009. http://dx.doi.org/10.1109/ciss.2009.5054762.

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Zhang, Mingli, and Christian Desrosiers. "Robust MRI reconstruction via re-weighted total variation and non-local sparse regression." In 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2016. http://dx.doi.org/10.1109/mmsp.2016.7813392.

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Meng, Dandan, Xianpeng Wang, Chong Shen, and Zhiguang Han. "DOA Estimation with Unknown Mutual Coupling for Monostatic MIMO Radar via Weighted Block Sparse Reconstruction." In 2020 IEEE International Conference on Computational Electromagnetics (ICCEM). IEEE, 2020. http://dx.doi.org/10.1109/iccem47450.2020.9219402.

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Pant, Jeevan K., Wu-Sheng Lu, and Andreas Antoniou. "Reconstruction of sparse signals by minimizing a re-weighted approximate ℓ0-norm in the null space of the measurement matrix." In 2010 53rd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2010. http://dx.doi.org/10.1109/mwscas.2010.5548758.

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Wang, Kaiqi, Ke Chen, and Kui Jia. "Deep Cascade Generation on Point Sets." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/517.

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This paper proposes a deep cascade network to generate 3D geometry of an object on a point cloud, consisting of a set of permutation-insensitive points. Such a surface representation is easy to learn from, but inhibits exploiting rich low-dimensional topological manifolds of the object shape due to lack of geometric connectivity. For benefiting from its simple structure yet utilizing rich neighborhood information across points, this paper proposes a two-stage cascade model on point sets. Specifically, our method adopts the state-of-the-art point set autoencoder to generate a sparsely coarse shape first, and then locally refines it by encoding neighborhood connectivity on a graph representation. An ensemble of sparse refined surface is designed to alleviate the suffering from local minima caused by modeling complex geometric manifolds. Moreover, our model develops a dynamically-weighted loss function for jointly penalizing the generation output of cascade levels at different training stages in a coarse-to-fine manner. Comparative evaluation on the publicly benchmarking ShapeNet dataset demonstrates superior performance of the proposed model to the state-of-the-art methods on both single-view shape reconstruction and shape autoencoding applications.
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Clark, S. E., and L. F. Desandre. "High Resolution Image Reconstruction from Sparse Random Arrays." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.wb4.

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In order to overcome such limitations as resolution, receiver weight and response time associated with large heavy monolithic laser radar receivers, a significant amount of work1-5 has been performed into the potential of array based systems to provide high resolution lightweight fast receivers. In order to achieve image resolution consistent with the overall size of the array, the amplitude and phase at each point in the array must be determined. As has been demonstrated in the microwave region6,7, the problem of phase errors due to errors in detector positioning and variations in the reference wavefront that are associated with the heterodyne detection process used to determine the phase, can be overcome in an adaptive way prior to using the array for imaging.
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