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Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due to the severe loss of information during the projection process. This paper proposes a two-stage passive NLOS imaging approach, aimed at reconstructing high-quality complicated hidden scenes. In the first stage, we train a coarse reconstruction network based on the optimal transport principle and using vector quantization to learn discrete priors for projection image encoding.