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Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments’ results, thereby improving the success rate of the experiments.

Abstract Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel, resulting in long rendering time. To reduce the cost, one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel (as little as 128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning: they use convolutional neural networks to learn the relationship between noisy images and reference images, using auxiliary features such as position and normal together with image color as inputs. The network predicts kernels which are then applied to the noisy input. These methods show powerful denoising ability, but tend to lose geometric or lighting details and to blur sharp features during denoising. In this paper, we solve this issue by proposing a novel network structure, a new input feature—light transport covariance from path space—and an improved loss function. Our network separates feature buffers from the color buffer to enhance detail effects. The features are extracted separately and then integrated into a shallow kernel predictor. Our loss function considers perceptual loss, which also improves detail preservation. In addition, we use a light transport covariance feature in path space as one of the features, which helps to preserve illumination details. Our method denoises Monte Carlo path traced images while preserving details much better than previous methods.

To improve the beam pointing accuracy of PW laser facility and reduce the optical axis deviation caused by the deflection amplitude response deviation of the confocal lens of spatial filter for microvibration action, an Optomechanical coupling vibration active control theory is proposed to make the peak value of output optical angle response lower than the pointing threshold value by 0.2 μrad. To establish an Optomechanical coupling vibration active control system, the active control parameters are introduced into the beam transmission matrix of the Optomechanical coupling system. The active control parameters and the peak value of the output light angle response are linked point to point. The algorithm flow of the active control system is designed, the control rules are established, and the control effect is verified. The results show that the peak value of the output optical angle response of the spatial filter decreases by 98.13%, and the attenuation is nearly two orders of magnitude after the active control, which effectively improves the convergence accuracy of the beam pointing of the spatial filter of the PW laser facility, and realizes the beam pointing control under the broadband excitation, and the control result is consistent with the expectation.