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Improved RL deconvolution algorithm based star map deblurring method

A deblurring and deconvolution technology, applied in computing, image enhancement, image analysis, etc., can solve the problems of poor image robustness and poor real-time performance, and achieve the effect of suppressing image blur and improving real-time performance

Active Publication Date: 2018-06-22
SOUTHEAST UNIV
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Problems solved by technology

[0005] Purpose of the invention: In order to overcome the deficiencies of the prior art, the present invention provides a star map deblurring method based on the improved RL deconvolution algorithm, which overcomes the poor real-time performance and the deblurring after deblurring caused by the artificial experience setting parameters of the traditional RL deconvolution algorithm. The problem of poor image robustness of

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[0024] Such as figure 1 As shown, the present invention discloses a star map deblurring method of an improved Richardson-Lucy (RL) deconvolution algorithm, comprising the following steps:

[0025] (1) In the star map simulator, set the field of view of the star sensor to 20°×20°, the focal length of the CMOS camera to 98.8mm, the CMOS area array to 512×512, the pixel width to 26μm, the pixel height to 26μm, and the star gray The degree value is 100, the star size is 4 pixel values, and it is set to randomly add false stars and random missing stars in the star map. The motion trajectory of the star sensor is simulated by the orbit generator, and a large number of clear stars are simulated using the above parameters. There is no specific requirement for the number of pictures, generally hundreds or thousands of pictures are simulated, and the star sensor dithering item is added to the above settings to simulate the corresponding fuzzy star map.

[0026] (2) Calculate the point ...

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Abstract

The invention discloses an improved RL deconvolution algorithm based star map deblurring method. The method includes (1) setting basic simulation parameters in a star map simulator and simulating a clear star map and a corresponding blurred star map by using the basic simulation parameters; (2) calculating the point spread function of each blurred star map according to inertia sensor element data;(3) implementing star map deblurring by adopting the RL deconvolution algorithm and recording an iteration frequency and an iteration step size of the deblurring effect optimization; (4)performing offline training on a neural network prediction model by taking the point spread function, the iteration frequency and the iteration step size of each blurred image as inputs and obtaining a predicationmodel after implementation of training and outputting the iteration frequency and the iteration step size. According to the invention, shortcomings that star map deblurring requires manual trial of the iteration frequency and the iteration step size by utilizing a traditional RL deconvolution deblurring algorithm is overcome, the real time performance of the algorithm is improved and star map deblurring effect is also improved distinctively.

Description

technical field [0001] The invention relates to a star map defuzzification method, in particular to a star map defuzzification method based on an improved RL deconvolution algorithm. Background technique [0002] The blurring of the star map is caused by the relative motion between the photosensitive medium and the starry sky at the moment of exposure. The limited starlight energy is scattered on more pixels, resulting in blurred star images and inaccurate positioning of the center of mass. For image motion compensation, there are currently mechanical, optical, electronic, and image image motion compensation methods. [0003] The image-based image motion compensation method is to restore the blurred star map to complete the image motion compensation, and it is also a compensation method for CMOS cameras. Different from mechanical, optical, and electronic methods, the image-based image motion compensation method performs post-processing on the generated digital image to eli...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T2207/10004G06T2207/20084G06T2207/20081G06T5/73
Inventor 陈熙源柳笛方文辉方琳
Owner SOUTHEAST UNIV
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