Target Reconstruction for Longitudinal Laser Tomography Through Multilayer Cloud and Fog Media

Range-gated laser imaging and longitudinal laser tomography (LLT) can suppress backscattering noise in turbid environments. However, when a target is obscured by multiple cloud or fog layers, the measured target image suffers from spatially nonuniform degradation, and single-layer restoration methods based on LLT cannot adequately compensate for the cumulative effects of the preceding layers. Here, we propose a layer-wise degradation modeling and compensation method for target reconstruction through multilayer cloud and fog media. In the LLT framework, range gating is used to acquire the backscattering images of individual medium layers and the degraded target image. Because the measured image of each layer is affected by the preceding medium layers, the layer images are recursively reconstructed using a total variation model. The reflectivity and transmission matrix of each layer are then estimated from the reconstructed layer image, pre-calibrated system parameters, and an estimated LIDAR ratio. The overall transmission matrix is obtained as the Hadamard product of the individual layer matrices and is incorporated into a total variation model to reconstruct the target image. Numerical simulations of targets obscured by two- and three-layer media demonstrate that the proposed method substantially improves reconstruction quality. The reconstructed images exhibit significant improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared with the degraded target images. Furthermore, quantitative comparisons with other restoration methods and robustness analyses of key parameters confirm the reliability of the framework. These results indicate that the proposed method provides a feasible and robust approach for target imaging through multilayer cloud, fog, and other turbid media.