Blind Infrared Remote-Sensing Image Deblurring Algorithm via Edge Composite-Gradient Feature Prior and Detail Maintenance

The problem of blind image deblurring remains a challenging inverse problem, due to the ill-posed nature of estimating unknown blur kernels and latent images within the Maximum A Posteriori (MAP) framework. To address this challenge, traditional methods often rely on sparse regularization priors to...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 16; číslo 24; s. 4697
Hlavní autoři: Zhao, Xiaohang, Li, Mingxuan, Nie, Ting, Han, Chengshan, Huang, Liang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2024
Témata:
Algorithms
blind image deblurring
Blurring
Datasets
Deep learning
detail maintenance
edge gradient feature
Effectiveness
Estimation
Ill posed problems
Information processing
Infrared imagery
Inverse problems
Iterative methods
Methods
Neural networks
Optimization
Regularization
Remote sensing
remote sensing images
Restoration
sparse prior term
Sparsity
ISSN:2072-4292, 2072-4292
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Shrnutí:The problem of blind image deblurring remains a challenging inverse problem, due to the ill-posed nature of estimating unknown blur kernels and latent images within the Maximum A Posteriori (MAP) framework. To address this challenge, traditional methods often rely on sparse regularization priors to mitigate the uncertainty inherent in the problem. In this paper, we propose a novel blind deblurring model based on the MAP framework that leverages Composite-Gradient Feature (CGF) variations in edge regions after image blurring. This prior term is specifically designed to exploit the high sparsity of sharp edge regions in clear images, thereby effectively alleviating the ill-posedness of the problem. Unlike existing methods that focus on local gradient information, our approach focuses on the aggregation of edge regions, enabling better detection of both sharp and smoothed edges in blurred images. In the blur kernel estimation process, we enhance the accuracy of the kernel by assigning effective edge information from the blurred image to the smoothed intermediate latent image, preserving critical structural details lost during the blurring process. To further improve the edge-preserving restoration, we introduce an adaptive regularizer that outperforms traditional total variation regularization by better maintaining edge integrity in both clear and blurred images. The proposed variational model is efficiently implemented using alternating iterative techniques. Extensive numerical experiments and comparisons with state-of-the-art methods demonstrate the superior performance of our approach, highlighting its effectiveness and real-world applicability in diverse image-restoration tasks.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs16244697