Image reconstruction method based on coded aperture and target

Abstract

The invention belongs to the field of computer vision technology and aims to propose an improved non-blind spot diffusion function estimation method to improve the quality of reconstructed images. In order to achieve the above objectives, the technical solution adopted by the present invention is an image reconstruction method based on coding aperture and target design, which includes the following steps: design the coding aperture and place it at the end of the camera lens near the detector; design the target and place it in the infrared image light source at the front end of the light tube; the point spread function of the infrared image is obtained through the fuzzy kernel solution algorithm model; by analyzing the infrared image feature model, L 0 The norm is used as an image regularization constraint, and the blurred image is reconstructed and restored based on the point spread function solved previously. The invention is mainly used in image processing situations.

Description

technical field [0001] The invention belongs to the technical field of computer vision, specifically, mainly relates to the fields of digital image processing and computational photography, and is an image reconstruction method based on coded aperture and target design. Background technique [0002] Nowadays, society has higher and higher requirements for image quality. Compared with visible light imaging systems, imaging systems have been more and more widely used in the fields of national economy, military defense and other fields due to their strong ability to penetrate smoke and water vapor. In space optical imaging, due to factors such as diffraction and aberration of the optical system itself, atmospheric turbulence, relative compound motion between the space camera and the shooting scene, and camera defocus, the image obtained by the camera will be blurred, affecting the image of interest. Interpretation of the target. With the improvement of imaging quality require...

AI Technical Summary

Problems solved by technology

The earliest regularization method, such as Tikhonov regularization method, uses L 2 They believe that the pixel value distribution of the image satisfies a quadratic function. Although this type of method can remove noise better, it is easy to lose image details.

For the problem of high complexity and slow operation speed of iterative algorithm

However, the above method involves many and complicated parameters, and they are all set according to empirical values, and there is no adaptive selection of parameters according to the structure and characteristics of the image.

Different image restoration only adopts the same parameter setting, which will inevitably lead to some images not being restored well, or the optimal solution cannot be achieved.

In order to solve the problem of inaccurate point spread function estimation in the image deblurring problem, and to better realize image restoration, the present invention uses the coding aperture to design the target to estimate the point spread function, and proposes an improved image reconstruction solution Model

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