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.

翻译：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图像进行去模糊处理，对于重建场景潜在的清晰图像至关重要。在过去十年中，盲卷积技术的出现通过其从获取图像中建模复杂特征的能力，彻底改变了这一领域。现有方法大多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，研究人员对于在高维环境空间中探索潜在图像的低维流形结构关注有限。本文提出了一种新颖的方法，在多帧盲卷积的框架内，利用流形拟合与惩罚来处理RSA图像。我们开发了快速算法来实现所提出的流程。仿真研究表明，基于流形的解卷积方法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，从而优于传统的解卷积算法。