PSF estimation method based on hybrid Gaussian model and sparse constraints

A mixed Gaussian model and sparse constraint technology, applied in computing, image data processing, instruments, etc.

Active Publication Date: 2017-04-26
LIAONING TECHNICAL UNIVERSITY
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  • Description
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Problems solved by technology

[0004] Although the above imaging methods have good results, they all have limitations

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  • PSF estimation method based on hybrid Gaussian model and sparse constraints
  • PSF estimation method based on hybrid Gaussian model and sparse constraints
  • PSF estimation method based on hybrid Gaussian model and sparse constraints

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[0066] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0067] Select a square area with good imaging effect from the entire observed image as the sample for the initial PSF estimation in this embodiment, such as figure 1 with figure 2 As shown, it is a 25×25 square area sample intercepted by a small window, from figure 2 The three-dimensional schematic diagram shown can clearly know the pixel values ​​in the row direction and the column direction.

[0068] In order to estimate the PSF during image imaging, this embodiment provides a PSF estimation method based on a Gaussian mixture model and sparse constraints, such as image 3 As shown, the steps are as follows:

[0069] Step S1: Read the entire observed optical image g(x, y), the expression is:

[0070]

[0071] Amo...

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Abstract

The invention provides a PSF estimation method based on a hybrid Gaussian model and sparse constraints and relates to the optical image restoration technology field. The method comprises steps that through extracting a surface-like region with relatively good imaging quality from an image, a PSF fitting function is established through utilizing the hybrid Gaussian model, an initial PSF template is acquired through model parameter solution, the template is taken as an initial iteration PSF template of a sparse constraint restoration model, and a final PSF template of an integral image is acquired through iteration. The method is advantaged in that no approximation processing is carried out in a processing process, main operation is carried out in an image space domain, repeated iterative computation is not required, and reliable and high efficiency PSF estimation can be realized.

Description

Technical field [0001] The invention relates to the technical field of optical image restoration, in particular to a PSF estimation method based on a mixture of Gaussian models and sparse constraints. Background technique [0002] The method of estimating the point spread function (PSF) of the image according to the imaging characteristics of the ground object can best reflect the degradation characteristics of the image, and can realize the optimal estimation of the entire image degradation model. Combined with the degradation characteristics of image imaging, the method of estimating PSF based on the imaging characteristics of actual ground objects is very suitable for the estimation of degradation models in image processing, such as: PSF-based image filtering denoising and image super-resolution reconstruction, image restoration Wait. [0003] Correspondingly, methods for estimating PSF based on the imaging characteristics of ground objects in optical images have been proposed ...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 卜丽静张正鹏张过
Owner LIAONING TECHNICAL UNIVERSITY
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