Affiliation:
1. Hubei Key Laboratory of Optical Information and Pattern Recognition
2. Huazhong University of Science and Technology
Abstract
Large field-of-view optical imaging systems often face challenges in the presence of space-variant degradation. The existence of degradation leads to target detection and recognition being difficult or even unsuccessful. To address this issue, this paper proposes an adaptive anisotropic pixel-by-pixel space-variant correction method. First, we estimated region acquisition of local space-variant point spread functions (PSFs) based on Haar wavelet degradation degree distribution, and obtained initial PSF matrix estimation with inverse distance weighted spatial interpolation. Then, we established a pixel-by-pixel space-variant correction model based on the PSF matrix. Third, we imposed adaptive sparse regularization terms of the Haar wavelet based on the adaptive anisotropic iterative reweight strategy and non-negative regularization terms as the constraint in the pixel-by-pixel space-variant correction model. Finally, as the correction process is refined to each pixel, the split-Bregman multivariate separation solution algorithm was employed for the pixel-by-pixel spare-variant correction model to estimate the final PSF matrix and the gray value of each pixel. Through this algorithm, the “whole image correction” and “block correction” is avoided, the “pixel-by-pixel correction” is realized, and the final corrected images are obtained. Experimental results show that compared with the current advanced correction methods, the proposed approach in the space-variant wide field correction of a degraded image shows better performance in preserving the image details and texture information.
Funder
National Natural Science Foundation of China
Knowledge Innovation Program of Wuhan Basic Research
Subject
Computer Vision and Pattern Recognition,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Cited by
1 articles.
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