A Novel Recovery Algorithm of Incomplete Observation Matrix for Converting 2-D Video to 3-D Content

2006 ◽  
pp. 260-269
Author(s):  
Sungshik Koh
Keyword(s):  
Recovery Algorithm ◽  
Observation Matrix

scholarly journals Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1079
Author(s):  
Vladimir Kazakov ◽  
Mauro A. Enciso ◽  
Francisco Mendoza
Keyword(s):  
Gaussian Processes ◽  
Research Method ◽  
General Algorithm ◽  
Two Dimensional ◽  
Recovery Algorithm ◽  
Conditional Mean ◽  
Quality Of Recovery ◽  
Input And Output ◽  
Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

Path Recovery Algorithm Using Fast-Failover for Software-Defined Networks

2020 ◽  
Author(s):  
Ian Vilar Bastos ◽  
Vinicius Correa Ferreira ◽  
Debora Christina Muchaluat-Saade ◽  
Celio Vinicius Neves de Albuquerque ◽  
Igor Monteiro Moraes
Keyword(s):  
Software Defined Networks ◽  
Recovery Algorithm

Processing of Sonar Image Based on Compressive Sensing

2011 ◽  
Vol 301-303 ◽  
pp. 719-723 ◽  
Author(s):  
Zhi Jing Xu ◽  
Huan Lei Dai ◽  
Pei Pei Cao
Keyword(s):  
Wavelet Transform ◽  
Matching Pursuit ◽  
Experimental Result ◽  
Underwater Acoustic Channel ◽  
Sonar Image ◽  
Underwater Image ◽  
Reconstruction Image ◽  
Observation Matrix ◽  
Efficient Transmission

The particularity of the underwater acoustic channel has put forward a higher request for collection and efficient transmission of the underwater image. In this paper, based on the characteristics of sonar image, wavelet transform is used to sparse decompose the image, and selecting Gaussian random matrix as the observation matrix and using the orthogonal matching pursuit (OMP) algorithm to reconstruct the image. The experimental result shows that the quality of the reconstruction image and PSNR have gained great ascension compared to the traditional compression and processing of image based on the wavelet transform while they have the same measurement numbers in the coding portion. It provides a convenient for the sonar image’s underwater transmission.

Step Size For The General Iterative Image Recovery Algorithm

1988 ◽  
Vol 27 (9) ◽  
Author(s):  
C. I. Podilchuk ◽  
R. J. Mammone
Keyword(s):  
Image Recovery ◽  
Step Size ◽  
Recovery Algorithm
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