A Blind Restoration Method of Defocused Blurred Image Based on Intermediate Frequency

Abstract

The present invention provides a blind restoration method for out-of-focus blurred images based on intermediate frequency. The steps of the blind restoration method are: calculating the frequency spectrum of the defocused blurred image; calculating the mean function of the ellipse trajectory of the frequency spectrum of the defocused blurred image; Variable-span smoothing filter" to obtain a benchmark function; use the ellipse track mean function to calculate the mid-frequency domain of the defocus blurred image spectrum; use the ellipse track mean function and its benchmark function to estimate the position of the first dark ring in the mid-frequency domain; use the estimated No. A dark ring position calculates the defocus radius and generates the defocus optical transfer function; restores the image with an intermediate frequency based Wiener filter. Compared with the prior art, the present invention has the beneficial effect of being able to automatically identify parameters such as the defocus radius from the defocused blurred image, realizing effective restoration of the defocused blurred image, and having strong noise suppression ability, few interactive parameters, and easy restoration. The advantage of speed.

Description

A Blind Restoration Method of Defocused Blurred Image Based on Intermediate Frequency technical field The invention belongs to the technical field of image blind restoration, and in particular relates to an intermediate frequency-based blind restoration method for defocused blurred images. Background technique Image restoration is a hot technology in recent years, which has been widely used in astronomy, military, medicine, remote sensing, television and other fields. Its purpose is to restore the original clear scene from the degraded observation image. The mathematical degradation model can be described as the convolution of the original image and the point spread function (PSF) in the presence of additive noise, as follows: Where x, y are the two-dimensional coordinates of the plane, g(x, y) is the gray level of the degraded image, f(x, y) is the gray level of the original clear image, and h(x, y) is the gray level of the degraded PSF Degree, z(x,y) is additive noise...

AI Technical Summary

Problems solved by technology

The technical problem to be solved by the present invention is: to overcome the problems of low identification accuracy, poor restoration effect, and slow restoration speed of the blur parameters of the defocused blurred image in the existing method, and to provide a defocused blurred image using the principle and method of the frequency domain of the image. A Fast Blind Restoration Method for Blurred Images

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