Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging
This work addresses image deblurring for rotating synthetic aperture imaging systems, offering an incremental improvement by incorporating low-dimensional manifold structures into multi-frame blind deconvolution.
The paper tackles the problem of deblurring rotating synthetic aperture (RSA) images to reconstruct sharp latent images, proposing a novel method based on manifold fitting and penalization that outperforms conventional algorithms by generating sharper estimates with better pixel intensity accuracy and structural detail preservation.
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.