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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

The technical effects provided through this method include improved recovery efficiency during reconstructing high resolution (HR) radar imagery data without sacrificing accuracy or reducing noise levels due to imperfections such as uneven ground conditions caused by waves hitting the surface being scanned. Additionally, there may also help reduce errors when combining different types of sensors together to create accurate 3D maps.

Problems solved by technology

This patents discusses how an algorithm called LYE helps restore sharpness when reconstructing scenes with imperfeled or distorted lenses (images). However, there may still exist some areas where no clear vision has been achieved even if we have good cameras. These areas include edges around windows, corners, etc., affecting visual clarity. To address this problem, algorithms like AutoCorrect were developed specifically designed to enhance edge detection capabilities during reconstruction processes.

Images