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Motion blurring and defocusing composite blurring image restoration method

A motion blur, image technology, applied in the field of image processing, can solve problems such as inability to directly apply

Inactive Publication Date: 2011-02-09
SOUTHEAST UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When motion blur and defocus appear at the same time, there is no sure composite model to refer to, and the above parameter estimation methods cannot be directly applied due to the mutual superposition of blur

Method used

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  • Motion blurring and defocusing composite blurring image restoration method

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Embodiment Construction

[0079] In a specific implementation, the detailed process of motion blur and defocus compound blur image restoration algorithm will be clearly and completely described in conjunction with the accompanying drawings:

[0080] An image restoration method for motion blur and defocus compound blur, comprising the steps of:

[0081] Step 1 Degraded image denoising

[0082] Take f(x,y) as M 1 × M 2 The original clear image of size, g(x,y) is M 1 × M 2 The degraded image of the size, h(x, y) is N 1 ×N 2 Motion blur and defocus compound blur model of size, n(x,y) is M 1 × M 2 Additive noise of size, where x and y are row coordinates and column coordinates respectively, and both are integers greater than 0, M 1 , N 1 is the number of rows, M 2 , N 2 is the number of columns, M 1 , M 2 , N 1 and N 2 Both are integers greater than zero, and the image degradation process is:

[0083] g(x,y)=f(x,y)*h(x,y)+n(x,y)

[0084] Where * is a well-known convolution operation, which ...

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Abstract

The invention provides a motion blurring and defocusing composite blurring image restoration method. By the method, parameter estimation and image restoration can be performed on a motion blurring and defocusing image, and the method comprises the following steps of: (1) establishing a gauss white noise template, and convoluting a degraded image and the white noise template to fulfill the aim of removing noise; (2) estimating a main blurring direction and a secondary blurring direction of the image through an image energy spectrum; (3) calculating a main directional derivative matrix and a secondary directional derivative matrix of the image; (4) performing self-correlation operation and directional accumulation operation on the main directional derivative matrix and the secondary directional derivative matrix respectively; (5) estimating a main direction blurring length and a secondary direction blurring length according to the self-correlated accumulation curve of a main directional derivative and the self-correlated accumulation curve of a secondary directional derivative; (6) establishing a composite blurring model according to the obtained main direction blurring length and secondary direction blurring length; and (7) restoring the degraded image by using wiener filtering.

Description

technical field [0001] The invention relates to an image restoration method of compound blur of motion blur and defocus. Its purpose is to establish a composite degraded model, estimate blur parameters, and implement image restoration when motion blur and defocus exist simultaneously in an image, which belongs to image processing field. Background technique [0002] In order to improve image quality, a model-based image restoration method is often used at present. The core of this method is to be able to accurately know the blur model and blur parameters. [0003] Motion blur and defocus are two common types of image blur. Motion blur is caused by the relative motion between the captured image and the camera equipment, while defocus is caused by the camera equipment being out of focus. For these two types of fuzziness, they are currently processed separately, estimating their fuzzy parameters and establishing a fuzzy model. The commonly used fuzzy parameter estimation metho...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 路小波李楠
Owner SOUTHEAST UNIV
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