Self-adaption optical image high resolution restoration method combining frame selection and blind deconvohtion

Abstract

The invention relates to a self-adaptive optical image high resolution restoration method combined with frame selection and blind deconvolution, comprising the following steps: firstly, a short exposure image sequence gn (x, y) is recorded when a self-adaptive optical closed loop is corrected; shannnon entropy of each frame of image in the sequence is calculated; a degraded image gm (x, y) with lower entropy is selected for blind deconvolution image restoration; secondly, an initial value hm (x, y) of a point spread function is generated by utilization of random phase; thirdly, a target f(x, y) is estimated by using the gm(x, y) and the obtained hm (x, y), and an estimated value f(x, y) is obtained after addition of positivity limitation on the target; fourthly, an estimated value hm (x, y) of the point spread function is obtained by using the gm (x, y) and the f(x, y), and an estimated value h(x, y) is obtained after addition of positivity limitation in the same way; fifthly, inspection is made whether an iterated value h(x, y) and an iterated value f(x, y) meet iteration stopping requirements or not; if the iterated values do not meet the iteration stopping requirements, the third step is returned; if the iterated values meet the iteration stopping requirements, circulation is stopped and the f(x, y) and the h(x, y) are outputted. The invention has the advantages of effective improvement of restoration quality, acceleration of convergence rate, capability of well compensating correction capability under hardware limitation of a self-adaptive optical system, and improvement of imaging quality.

Description

technical field [0001] The invention belongs to the image processing technology, and proposes a blind image restoration technology aimed at partially correcting images of an adaptive optical system. Background technique [0002] Image restoration is the process of estimating real images from observed degraded images. Its imaging model can be expressed as [0003] g ( x , y ) = f ( x , y ) ⊗ h ( x , y ) + n ( x , y ) - - - ( 1 ) [0004] In the formula, g(x, y) represents the observed degraded image; f(x, y) represents the real tar...

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Problems solved by technology

[0006] Liang Ying et al. proposed to use blind deconvolution to post-process partially corrected adaptive optics images, but did not consider that not all recorded degraded images can be improved by deconvolution; J.J.Green et al. It has been proposed to use frame selection techniques to post-process short-exposure sequences, but they only use the data in the wavefront sensor for deconvolution, without considering that the wavefront sensor itself is affected by noise and cannot fully represent the wavefront phase difference; they also Secondary corrections to images partially corrected by adaptive optics are not considered

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