24 May 2017 Blind restoration for nonuniform aerial images using nonlocal Retinex model and shearlet-based higher-order regularization
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Abstract
Aerial images are often degraded by space-varying motion blurs and simultaneous uneven illumination. To recover a high-quality aerial image from its nonuniform version, we propose a patchwise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illumination conditions. A nonlocal Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter, the uneven illumination is corrected well. Then nonuniform coupled blurring in the enhanced reflectance image is alleviated and transformed toward uniform distribution, which will facilitate the subsequent deblurring. For constructing the multiscale sparsified regularization, the discrete shearlet transform is improved to better represent anisotropic image features in terms of directional sensitivity and selectivity. In addition, a new adaptive variant of total generalized variation is proposed to act as the structure-preserving regularizer. These complementary regularizers are elegantly integrated into an objective function. The final deblurred image with uniform illumination can be obtained by applying a fast alternating direction scheme to solve the derived function. The experimental results demonstrate that our algorithm can not only effectively remove both the space-varying illumination and motion blurs in aerial images, but also recover the abundant details of aerial scenes with top-level objective and subjective quality, and outperforms other state-of-the-art restoration methods.
© 2017 SPIE and IS&T
Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao, "Blind restoration for nonuniform aerial images using nonlocal Retinex model and shearlet-based higher-order regularization," Journal of Electronic Imaging 26(3), 033016 (24 May 2017). https://doi.org/10.1117/1.JEI.26.3.033016 . Submission: Received: 30 September 2016; Accepted: 13 April 2017
Received: 30 September 2016; Accepted: 13 April 2017; Published: 24 May 2017
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