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红外成像系统由于探测器加工工艺的限制,很难通过减小像元尺寸或增加阵元数量的方式实现高分辨率成像。压缩感知理论提供了一种新的提高成像分辨率的方法,在光学系统的焦平面处放置编码掩膜,使得红外探测器得到的图像是被观测场景的压缩采样,再通过稀疏优化算法重构出原始图像。决定图像分辨率的不是探测器的像素尺寸,而是编码掩膜的孔径大小。在此框架下,设计了合适的光学编码掩膜子阵,利用多路技术实现了对同一场景的多次压缩采样,采用了线性Bregman迭代思想进行重构算法的设计,解决了二维成像大规模重构算法的求解速度和精度问题。数值仿真表明,该方法在保证图像重构质量的前提下可显著提高红外成像的分辨率。
Infrared imaging system due to the limitations of the detector processing technology, it is difficult to reduce the pixel size or increase the number of array elements to achieve high-resolution imaging. Compressive sensing theory provides a new method to improve imaging resolution by placing a coding mask at the focal plane of the optical system so that the image obtained by the infrared detector is compressed and sampled by the observed scene and then reconstructed by a sparse optimization algorithm Out of the original image. Instead of the pixel size of the detector that determines the resolution of the image, it is the size of the aperture of the encoded mask. In this framework, the design of a suitable optical encoding mask sub-array, the use of multiplexing technology to achieve the same scene multiple compression sampling, the use of linear Bregman iterative algorithm for reconstruction algorithm design to solve the large two-dimensional imaging Problem of solving speed and accuracy of scale reconstruction algorithm. Numerical simulation shows that this method can significantly improve the resolution of infrared imaging under the premise of ensuring the quality of image reconstruction.