scholarly journals Tensor factorization for model-free space-variant blind deconvolution of the single- and multi-frame multi-spectral image

2010 ◽  
Vol 18 (17) ◽  
pp. 17819 ◽  
Author(s):  
Ivica Kopriva
Keyword(s):  
Free Space ◽  
Blind Deconvolution ◽  
Tensor Factorization ◽  
Spectral Image ◽  
Model Free ◽  
Space Variant

Holographic space-variant prism arrays for free-space data permutation in digital optical networks

1991 ◽  
Author(s):  
Harald Kobolla ◽  
Frank Sauer ◽  
Johannes Schwider ◽  
Norbert Streibl ◽  
Reinhard Voelkel
Keyword(s):  
Optical Networks ◽  
Free Space ◽  
Space Data ◽  
Space Variant

scholarly journals Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Michal Jancosek ◽  
Tomas Pajdla
Keyword(s):  
Surface Reconstruction ◽  
Free Space ◽  
State Of The Art ◽  
The State ◽  
Real Object ◽  
Reconstruction Method ◽  
The Real ◽  
Model Free ◽  
Novel Method ◽  
Different Levels

We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.

scholarly journals Scattering correction through a space-variant blind deconvolution algorithm

2016 ◽  
Vol 21 (9) ◽  
pp. 096005 ◽  
Author(s):  
Koberstein-Schwarz Benno ◽  
Omlor Lars ◽  
Schmitt-Manderbach Tobias ◽  
Mappes Timo ◽  
Ntziachristos Vasilis
Keyword(s):  
Blind Deconvolution ◽  
Deconvolution Algorithm ◽  
Scattering Correction ◽  
Space Variant
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