The compressed ultrafast photography (CUP) technique, providing the fastest receive-only camera so far, has shown to be a well-established tool to capture the ultrafast dynamical scene. This technique is based on random codes to encode and decode the ultrafast dynamical scene by a compressed sensing algorithm. The choice of random codes significantly affects the image reconstruction quality. Therefore, it is important to optimize the encoding codes. Here, we develop a new scheme to obtain the optimized codes by combining a genetic algorithm (GA) into the CUP technique. First, we measure the dynamical scene by the CUP system with random codes and obtain the dynamical scene image at each moment. Second, we use these reconstructed dynamical scene images as the optimization target and optimize the encoding codes based on the GA. Finally, we utilize the optimized codes to recapture the dynamical scene and improve the image reconstruction quality. We validate our optimization scheme by the numerical simulation of a moving double-semielliptical spot and the experimental demonstration of a time- and space-evolving pulsed laser spot.

中文翻译：

---

利用遗传算法优化压缩超快摄影代码

压缩超快摄影（CUP）技术提供了迄今为止最快的仅接收摄像头，已被证明是一种捕获超快动态场景的成熟工具。该技术基于随机码，通过压缩传感算法对超快动态场景进行编码和解码。随机码的选择会显着影响图像重建质量。因此，优化编码代码很重要。在这里，我们开发了一种通过将遗传算法（GA）组合到CUP技术中来获得优化代码的新方案。首先，我们使用随机代码通过CUP系统测量动态场景，并在每个时刻获取动态场景图像。其次，我们将这些重建的动态场景图像用作优化目标，并基于GA优化编码代码。最后，我们利用优化的代码重新捕获动态场景并提高图像重建质量。我们通过运动的双半椭圆形光斑的数值模拟以及时空演化的脉冲激光光斑的实验演示，验证了我们的优化方案。