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14 February 2020 A restoration method for the turbulent degraded images based on the salient edge selection and the L0 norm constraint
Yayuan Feng, Yu Shi, Dianjun Sun, Meixuan Wang
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Proceedings Volume 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis; 114280G (2020) https://doi.org/10.1117/12.2536579
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
Abstract
At present, the restoration of turbulent degraded images is a worldwide problem in the fields of astronomical imaging. Atmospheric turbulence is the reason why images will be blurred, which gravely interferes the object of recognition and the detection of images. This paper presents a restoration method for the turbulent degraded images based on the image saliency edge selection and the L0 norm constraint, which aims to recover sharp images from the turbulent degraded images. The proposed method imposes the L0 norm sparse constraint on the latent image, and uses the method of split Bregman to solve the problem of optimization. To avoid the influence of the tiny details on the point spread function (PSF) , we use the image saliency algorithm to build a weighted model to select salient edges from the latent images. Based on the salient edge in the gradient domain, the proposed method establishes an estimation model of the point spread function. The calculation part of the point spread function is solved accurately by using the fast Fourier transform (FFT) in the frequency domain. The proposed method uses the multi-scale pyramid strategy to alternatively solve the point spread function and the latent images, which can obtain the final accuracy of the point spread function.
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Yayuan Feng, Yu Shi, Dianjun Sun, and Meixuan Wang "A restoration method for the turbulent degraded images based on the salient edge selection and the L0 norm constraint", Proc. SPIE 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis, 114280G (14 February 2020); https://doi.org/10.1117/12.2536579
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KEYWORDS
Point spread functions
Image processing
Image restoration
Information visualization
Atmospheric turbulence
Image quality
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