Indirect time-of-flight (iToF) imaging provides absolute depth by encoding light transport delays into correlation measurements via active illumination modulation and demodulation. This technology plays a crucial role in object detection and scene understanding and has been widely adopted in applications such as robotics, automotive driving, and augmented reality. However, iToF systems often suffer from low signal-to-noise ratio (SNR), particularly under strong ambient illumination. Signal attenuation and noise-induced phase errors severely degrade depth accuracy, while reliable depth discontinuity and edge reconstruction remain challenging. In this paper, we introduce an optimal visual ToF imaging scheme, optimized through end-to-end learning of iToF coding functions and depth reconstruction, guided by discriminative Fisher information supervision. By integrating vision information from RGB images, we facilitate the convergence of the optimization for iToF coding functions and enhance robustness under noisy inputs. Moreover, we design a dual-branch visual ToF reconstruction network that estimates depth from iToF measurements while exploiting edge guidance derived from visual information to preserve fine geometric details. Extensive experiments on both synthetic and real-world datasets verify the effectiveness of the proposed approach, particularly in challenging low-SNR conditions.

Fluorescence lifetime imaging (FLIM) is widely used in biomedical applications as a powerful technique for resolving fluorophores and their unique molecular environments. Compared to Time-Domain FLIM (TD-FLIM), Frequency-Domain FLIM (FD-FLIM) allows for faster acquisition of fluorescence lifetime images, making it better suited for live-cell imaging and real-time applications. By illuminating a sample with modulated light and analyzing the phase shift and modulation reduction of the emitted fluorescence, the lifetimes of fluorophores in the sample can be determined. How to retrieve the optimal modulation function for FD-FLIM for noise-robust FLIM is still an open problem and the exposure time allocation for measurements of different coding functions has not been explored yet. In this paper, we propose an end-to-end learnable framework to jointly optimize the coding functions, the allocation of exposure time for measurements of different coding functions, and the reconstruction networks. Specifically, we propose a differential forward model of FD-FLIM with learnable allocation of exposure time. Besides, we propose a transformer-based FLIM reconstruction network to retrieve accurate fluorescence lifetime. The effectiveness of the proposed FD-FLIM method is extensively validated on both simulated and real-world fluorescence lifetime datasets, and a prototype imaging system has also been built to further demonstrate the proposed method.

Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution. However, lower signal-to-noise ratio (SNR) of measurement leads to larger error in ToF imaging, especially for imaging scenes with strong ambient light or long distance. In this paper, we propose a Fisher-information guided framework to jointly optimize the coding functions (light modulation and sensor demodulation functions) and the reconstruction network of iToF imaging, with the super-vision of the proposed discriminative fisher loss. By introducing the differentiable modeling of physical imaging process considering various real factors and constraints, e.g., light-falloff with distance, physical implementability of coding functions, etc., followed by a dual-branch depth reconstruction neural network, the proposed method could learn the optimal iToF imaging system in an end-to-end manner. The effectiveness of the proposed method is extensively verified with both simulations and prototype experiments.