Efficient Circular and Confocal Non-Line-Of-Sight Imaging With Transient Sinogram Super Resolution

Abstract

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Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e.g., walls) to see around corners. It has many potential applications, including autonomous driving, search and rescue, and medical imaging. The efficient utilization of NLOS imaging in these applications requires quick measurement and imaging. So far, unlike traditional 2D raster scanning, circular and confocal non-line-of-sight ($\mathrm{C}^{2} \mathrm{NLOS}$) utilizes 1D circular scanning for faster measurements and memory-efficient imaging. However, this technique’s limitation to circular measurements causes spatial bias in the gathered data, affecting the reconstruction result quality. To address this issue, we propose introducing learning-based image enhancement to $\mathrm{C}^{2}$ NLOS. To this end, we propose E-C ${ }^{2} \mathrm{NLOS}$ (Enhanced $\mathrm{C}^{2} \mathrm{NLOS}$), a framework in which we directly enhance the measured information. In our framework, we propose the Cropping module and Stitching module for faster and more effective image processing. Furthermore, considering that existing learning-based image enhancement methods are trained only on natural images, our work involves generating a synthetic dataset specifically designed for transient images, which is used for fine-tuning. Our experiments suggest that the framework works effectively, and our method outperforms the baseline method.