Laser reflection tomography algorithm based on photon detection characterization echo

Traditional optical imaging systems are limited by diffraction and pixel resolution, while computational imaging techniques have overcome these constraints, emerging as an effective means for super-resolution imaging. Laser Reflection Tomography (LRT), as an active detection method, utilizes laser pulses to illuminate targets from multiple angles and reconstructs images based on the Fourier Slice Theorem. The imaging resolution of LRT is determined solely by signal-to-noise ratio, laser pulse width, and detector bandwidth, and is not affected by detection distance or optical aperture. However, when detecting distant non-cooperative targets, the echo signal energy of LRT decays rapidly with increasing distance, significantly reducing the signal-to-noise ratio and sharply degrading image quality, making it difficult to meet the demands of super-resolution imaging. Leveraging the immense potential of single-photon detection for long-range detection and complex scene perception, this paper proposes a long-range LRT imaging algorithm based on single-photon detection echoes, which includes: (1) The photon detection probability waveform is obtained by calculating the number of photons in each time grid at each detection angle.(2) The waveform data under a series of detection angles are converted into reconstructed targets using algorithms such as filtered back projection.(3) Compare the quality of reconstructed images under different target scales and photon numbers, and analyze the factors affecting the reconstruction quality. Simulation experiments have demonstrated that the algorithm achieves image reconstruction based on photon detection echo characterization for the target model, effectively addressing the issue of image quality degradation due to weak echo energy, and holds significant research value and application potential in long-range detection imaging and space exploration.