imaging model

imaging model

A set of rules for representing images.
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According to the Kronecker structure of SAR imaging model and the blocks structural characteristics of scatterers, in this section, we will propose our SAR imaging algorithm based on the block sparsity feature with Kronecker dictionary.
For example, during your visit Eureka Dental Group takes both physical impressions of your teeth and do a computer imaging model, so you're getting the best fit and look possible.
Currently, most of image defogging algorithms are based on image restoration, the core idea of which is as follows: firstly, an imaging mode should be established; secondly, the degraded part of the imaging model is compensated and the interferential part of it is filtered; thirdly, the clear image is restored [5-17].
Generally, these calibration methods can be classified into parametric (defined by several intrinsic parameters) [14, 17, 18, 23-33] and general nonparametric calibration methods according to the imaging model [33-35].
This can result in a best practice imaging model that can be of significant help in positioning the HDO for value-based reimbursement changes that are coming its way.
Imaging Model. The imaging process is based on a mathematical model that describes the physical process of obtaining L LR images [y.sub.k], k = 1, ..., L, from the latent HR image x we desire.
Set the coordinate of a space point P at camera coordinate system [O.sub.C][X.sub.C][Y.sub.C][Z.sub.C] as ([x.sub.c], [y.sub.c], [z.sub.c]); then the imaging model of the point P is represented as
Instead of using an idealised imaging model like in traditional filtered back-projection (FBP), these IR techniques use an imaging model that describes the image acquisition, including noise statistics, which makes these algorithms more efficient and opens up the possibility of noise reduction [12-17].
To overcome the drawback, an imaging model based on total variation constraint and nonlocal operator under compressed sensing theroy (hereinafter referred to as CSNL) is proposed in this paper.
Considering the normal optical imaging model, first of all, the proper adaptive optics imaging model is set up, which lays the foundation of restoring high-quality adaptive optical images.
Thirdly, the method based on imaging model [1, 26, 27], which formulates optimization functions on the basis of real imaging system.

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