Computational Imaging for conformal imaging systems

传统的成像系统相对较大，通常在一个近似长方体的成像系统中使用单个大透镜。这使得它们很难集成到许多平台上，特别是那些使用极端空气动力学表面的平台，如鼻锥。该项目的目标是开发计算成像系统，使用格拉斯哥数学模型开发的多孔径技术演示从保形表面进行高分辨率成像，包括：(1)非对称傅里叶域多相机成像。下面的模型说明了这一主题的变化，展示了如何将多个矩形光圈组合在一起，以产生各向同性的高分辨率成像。(2)用于消除气动表面成像引入的像差的计算成像。这项工作的一个关键目标是开发优化的成像系统，以检测关键对象，而不是检测图像。在这种情况下，诸如机器学习之类的技术可以直接对用于检测目标的像差、叠加图像进行操作。

Conventional imaging systems are relatively bulky and typically employ a single large lens within an approximately cuboid imaging systems. This makes them difficult to integrate into many platforms, particularly those employing extreme aerodynamic surfaces such as nose cones. This project will aim to develop computational imaging systems that demonstrate high-resolution imaging from conformal surfaces using multi-aperture techniques developed as mathematical models at Glasgow, including: (1) Asymmetric Fourier-domain multicamera imaging. Variations of this theme are illustrated in the models below showing how multiple rectangular apertures can be combined to yield isotropic, high-resolution imaging. (2) Computational imaging for mitigation of aberrations introduced by imaging through aerodynamic surfaces. A key aim in this work is develop imaging systems optimised for detection of key objects rather than for detection of images. In this case, techniques such as machine learning can operate directly on aberrated, superimposed images for detection of targets.