Fast Pre-Training Kernel Estimation Network for ISAR Space Target Image Deblurring
2023 8th International Conference on Image, Vision and Computing (ICIVC)(2023)
摘要
In inverse synthetic aperture radar (ISAR) imaging applications, the lack of image clarity often limits their usage in down-streaming tasks such as recognition and component judgment. The super-resolution of ISAR images after imaging needs to consider the uncertainty of the blur kernel and cannot simply use traditional super-resolution (SR) methods. Moreover, ISAR images are scarce, expensive to obtain, and pre-training based on a large number of images is challenging. Therefore, this paper proposes a Single-Image Super-Resolution (SISR) method with generalization ability for different blur kernels, which does not rely on a large amount of image data training for ISAR image super-resolution. A large number of experiments show that the proposed method is superior to the traditional Bicubic algorithm and learning-based DIP, Double-DIP methods, and achieves significantly better performance with less pre-training time.
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关键词
ISAR image super-resolution,deblurring,deep learning
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