AI 中文总结
该研究针对低采样率下计算鬼成像质量下降问题，提出深度学习框架优化照明图案，实验表明其性能优于随机图案，且图案可跨数据集迁移，为减少成像测量次数提供新方法。
AI 中文摘要
计算鬼成像（CGI）通过已知照明图案和桶探测器测量值重建物体，但在低采样率（SR）下成像质量会下降。本文提出一种深度学习框架，在重建前优化灰度散射体图案。在使用CIFAR-10和MNIST图像结合Split Bregman重建的模拟实验中，学习得到的图案在峰值信噪比和结构相似性上优于随机图案，包括在SR低于5%的情况下；在CIFAR-10上训练的图案也可迁移到MNIST，且在感知矩阵的适度扰动下仍有效。这些结果表明，学习型图案设计是减少CGI测量次数的可行途径。
英文摘要
Computational ghost imaging (CGI) reconstructs objects from known illumination patterns and bucket-detector measurements, but quality deteriorates at low sampling ratios (SRs). We present a deep-learning framework that optimizes grayscale diffuser patterns before reconstruction. In simulations using CIFAR-10 and MNIST images with Split Bregman reconstruction, the learned patterns outperform random patterns in peak signal-to-noise ratio and structural similarity, including at SRs below 5\%. Patterns trained on CIFAR-10 also transfer to MNIST and remain effective under moderate perturbations of the sensing matrix. These results support learned pattern design as a route to fewer CGI measurements.
Comments12 pages, 8 figures, comments are welcome!
