Low-Rank Phase Retrieval
نویسندگان
چکیده
منابع مشابه
Retrieval Performance Improvement through Low Rank Corrections
Whenever a feature extracted from an image has a unimodal distribution, information about its covariance matrix can be exploited for content-based retrieval using as dissimilarity measure the Bhattacharyya distance. To reduce the amount of computations and the size of logical database entry, we approximate the Bhattacharyya distance taking into account that most of the energy in the feature spa...
متن کاملGraph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful representation of the spectral response. Then, graph regularization is incorporated into the LRR model to capture the local structure information and the nonlinear property of the remote-sensing data. Since it is easy...
متن کاملMotion Retrieval Using Low-Rank Subspace Decomposition of Motion Volume
This paper proposes a novel framework that allows for a flexible and an efficient motion capture data retrieval in huge motion capture databases. The method first converts an action sequence into a novel representation, i.e. the Self-Similarity Matrix (SSM), which is based on the notion of self-similarity. This conversion of the motion sequences into compact and low-rank subspace representation...
متن کاملPhase retrieval from low rate samples
The paper considers the phase retrieval problem in N dimensional complex vector spaces. It provides two sets of deterministic measurement vectors which guarantee signal recovery for all signals, excluding only a specific subspace and a union of subspaces, respectively. A stable analytic reconstruction procedure of low complexity is given. Additionally it is proven that signal recovery from thes...
متن کاملBeyond Low Rank + Sparse: A Multi-scale Low Rank Decomposition
We present a multi-scale version of low rank matrix decomposition. Our motivation comes from imaging applications, in which image sequences are correlated locally on several scales in space and time rather than globally. We model our data matrix as a sum of matrices, where each matrix has increasing scales of locally low-rank matrices. Using this multi-scale modeling, we can capture different s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2017
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2017.2684758