Opt Express. 2021 Oct 25;29(22):36813-36827. doi: 10.1364/OE.439704.
Ghost imaging (GI) reconstructs images using a single-pixel or bucket detector, which has the advantages of scattering robustness, wide spectrum, and beyond-visual-field imaging. However, this technique needs large amounts of measurements to obtain a sharp image. Numerous methods are proposed to overcome this disadvantage. Retina-like patterns, as one of the compressive sensing approaches, enhance the imaging quality of the region of interest (ROI) while maintaining measurements. The design of the retina-like patterns determines the performance of the ROI in the reconstructed image. Unlike the conventional method to fill in ROI with random patterns, optimizing retina-like patterns by filling in the ROI with the patterns containing the sparsity prior of objects is proposed. The proposed method is then verified by simulations and experiments compared with conventional GI, retina-like GI, and GI using patterns optimized by principal component analysis. The method using optimized retina-like patterns obtains the best imaging quality in ROI among other methods. Meanwhile, the good generalization capability of the optimized retina-like pattern is also verified. The feature information of the target can be obtained while designing the size and position of the ROI of retina-like patterns to optimize the ROI pattern. The proposed method facilitates the realization of high-quality GI.
鬼成像（GI）使用单像素或桶式探测器重建图像，具有抗散射、宽谱和视场外成像的优点。然而，该技术需要大量的测量才能获得清晰的图像。提出了许多方法来克服这一缺点。类视网膜模式作为压缩感知方法之一，在保持测量的同时，提高了感兴趣区域（ROI）的成像质量。类视网膜模式的设计决定了重建图像中 ROI 的性能。与传统的用随机模式填充 ROI 的方法不同，提出了用包含物体稀疏先验的模式来填充 ROI 来优化类视网膜模式。然后，通过与传统 GI、类视网膜 GI 和基于主成分分析优化的 GI 进行模拟和实验比较，验证了所提出的方法。与其他方法相比，使用优化的类视网膜模式的方法在 ROI 中获得了最佳的成像质量。同时，还验证了优化的类视网膜模式具有良好的泛化能力。通过设计类视网膜模式的 ROI 的大小和位置，可以获取目标的特征信息，从而优化 ROI 模式。该方法有助于实现高质量的 GI。
