利用低维流形先验提升旋转合成孔径图像的去模糊效果
Multi-Frame Blind Manifold Deconvolution for Rotating Synthetic Aperture Imaging
- 基于多帧盲卷积，引入图像内容的低维流形约束
- 仿真显示新方法在像素精度和结构保真度上优于传统算法
- 适合从事高分辨率成像与逆问题求解的研究者
旋转合成孔径（RSA）成像系统通过旋转矩形孔径在不同角度捕获目标场景图像。对获取的RSA图像进行去模糊是重建潜在清晰图像的关键步骤。过去十年中，盲卷积技术的出现革新了该领域，能够从观测图像中建模复杂特征。现有方法大多通过最大化后验概率求解这一不适定逆问题，但较少关注高维空间中潜在图像的低维流形结构。本文提出一种新型多帧盲卷积方法，结合流形拟合与正则化，实现对RSA图像的处理，并开发了快速算法。模拟实验表明，基于流形的去卷积方法在像素强度估计和结构细节保持方面优于传统算法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。