Method for restoring TDICCD image aiming at motion fuzzy core space shift variant

Abstract

The invention discloses a method for restoring a TDICCD image aiming at motion fuzzy core space shift variant, which comprises the following steps of: performing interpolation fitting on a motion track of a TDICCD camera measured by a position detection device, and extracting a motion track section during exposure integration of each row of pixels in a TDICCD image time sequence from the motion track after the interpolation fitting; extracting the integral pixel deformation quantity of the pixels of the current row and a fuzzy core from the motion track section; positioning a pixel block to be restored containing information on the pixels of the current row in the TDICCD image; and substituting the pixel block to be restored corresponding to the pixels of the current row and the fuzzy core in the steps into a standard RL algorithm to perform restoration operation on each row of the pixels so as to obtain each row of the pixels after restoration, and recombining each row of the pixels after restoration to obtain a restored TDICCD image. The method can restore image degradation and fuzziness caused by motions of any forms to obtain clear undeformed restored images.

Description

technical field [0001] The invention relates to the field of TDICCD imaging and computer image processing technology, in particular to a digital image deblurring method. Background technique [0002] TDICCD (time delay and integration charge coupled device) imaging device has the advantages of high resolution, high sensitivity, and high signal-to-noise ratio, so it is widely used in various imaging occasions, especially in remote sensing fields such as aerospace. But it does not solve the problem of image degradation when the platform on which the imaging equipment is mounted has chatter interference. Moreover, the working mode of TDICCD push-broom imaging still has the problem of image definition degradation, and there may be superimposition and misalignment between image lines. For image restoration algorithms with known blur kernels, there are many classic excellent algorithms, such as RL, Wiener filtering, and overall variational regularization algorithms. Generally sp...

AI Technical Summary

Problems solved by technology

However, these algorithms cannot be directly used to restore TDICCD images, because these methods are based on the assumption that an image has only one blur kernel, and for TDICCD images, the motion forms experienced by each row of images during the exposure integration process are possible. different, their blur kernels may also be different

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