旋转合成孔径成像的多帧盲流形反卷积
Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging
AI总结：
针对旋转合成孔径图像去模糊问题，提出一种基于流形拟合与惩罚的多帧盲反卷积新方法，并开发了快速算法，仿真表明其能更精确估计像素强度并保留结构细节，优于传统方法。
摘要：
旋转合成孔径（RSA）成像系统通过旋转矩形孔径在不同旋转角度下捕获目标场景的图像。对获取的RSA图像进行去模糊在重建场景背后的潜在清晰图像中起着关键作用。在过去十年中，盲卷积技术的出现凭借其对获取图像中复杂特征的建模能力，彻底改变了这一领域。现有的大多数方法试图通过最大化后验概率来解决上述不适定逆问题。尽管取得了这些进展，研究人员对探索高维环境空间中潜在图像的低维流形结构关注有限。在此，我们提出了一种在多帧盲卷积背景下使用流形拟合和惩罚来处理RSA图像的新方法。我们开发了用于实现所提流程的快速算法。仿真研究表明，基于流形的反卷积在估计像素强度和保留结构细节方面能够生成更清晰的潜在图像估计，从而优于传统的反卷积算法。
英文摘要：
Rotating synthetic aperture (RSA) imaging system captures images of the target scene at different rotation angles by rotating a rectangular aperture. Deblurring acquired RSA images plays a critical role in reconstructing a latent sharp image underlying the scene. In the past decade, the emergence of blind convolution technology has revolutionised this field by its ability to model complex features from acquired images. Most of the existing methods attempt to solve the above ill-posed inverse problem through maximising a posterior. Despite this progress, researchers have paid limited attention to exploring low-dimensional manifold structures of the latent image within a high-dimensional ambient-space. Here, we propose a novel method to process RSA images using manifold fitting and penalisation in the content of multi-frame blind convolution. We develop fast algorithms for implementing the proposed procedure. Simulation studies demonstrate that manifold-based deconvolution can outperform conventional deconvolution algorithms in the sense that it can generate a sharper estimate of the latent image in terms of estimating pixel intensities and preserving structural details.
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