Weighted multi-scale denoising via adaptive multi-channel fusion for compressed ultrafast photography

Abstract

Being capable of passively capturing transient scenes occurring in picoseconds and even shorter time with an extremely large sequence depth in a snapshot, compressed ultrafast photography (CUP) has aroused tremendous attention in ultrafast optical imaging. However, the high compression ratio induced by large sequence depth brings the problem of low image quality in image reconstruction, preventing CUP from observing transient scenes with fine spatial information. To overcome these restrictions, we propose an efficient image reconstruction algorithm with multi-scale (MS) weighted denoising based on the plug-and-play (PnP) based alternating direction method of multipliers (ADMM) framework for multi-channel coupled CUP (MC-CUP), named the MCMS-PnP algorithm. By removing non-Gaussian distributed noise using weighted MS denoising during each iteration of the ADMM, and adaptively adjusting the weights via sufficiently exploiting the coupling information among different acquisition channels collected by MC-CUP, a synergistic combination of hardware and algorithm can be realized to significantly improve the quality of image reconstruction. Both simulation and experimental results demonstrate that the proposed adaptive MCMS-PnP algorithm can effectively improve the accuracy and quality of reconstructed images in MC-CUP, and extend the detectable range of CUP to transient scenes with fine structures.

基于免训练自监督式神经网络算法实现压缩超快成像高保真图像重构研究

“…关于R/ρ的近端算子 的形式. 为了防止DIP过拟合, 我们结合PnP设计, 使用 图像降噪算法(例如TV [42] 、FFDNet [43] 和DRUNet [44] )替 DIP、DIP-TV、DIP-FFDNet和DIP-DRUNet外, 还包括 在传统迭代算法中具有最佳重构性能的DeSCI算法 [41] , 量与驱动电流匹配良好, 等离子体的内爆是同步的 [47] . 、光 谱-立体压缩超快成像(spectral-volumetric CUP, SV-CUP) [49] 等多维成像模型, 实现更高维数据的恢复, 扩 大CUP类技术在捕捉复杂超快物理事件方面的应用范 围.…”