Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging

TL;DR

This paper introduces a novel manifold-based deconvolution method for rotating synthetic aperture imaging, improving image sharpness and structural detail preservation over traditional techniques.

Contribution

It presents a new approach leveraging low-dimensional manifold structures in multi-frame blind convolution for RSA image deblurring, with fast algorithms for implementation.

Findings

Manifold-based deconvolution outperforms conventional methods in image sharpness.

The proposed method better preserves structural details.

Simulation results validate the effectiveness of the approach.

Abstract

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.