Feature enhanced non-line-of-sight imaging using graph model in latent space

• Published in: Optics and laser technology Vol. 181; p. 111538
• Main Authors: Xu, Weihao, Chen, Songmao, Wang, Dingjie, Tian, Yuyuan, Zhang, Ning, Hao, Wei, Su, Xiuqin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Graph model; Non line of sight imaging; Regularization by denoising; Non line of sight imaging; Graph model; Regularization by denoising
• ISSN: 0030-3992
• DOI: 10.1016/j.optlastec.2024.111538

Non-line-of-sight (NLoS) imaging reveals hidden scene from indirect diffusion signals. However, it is still challenging to balance noise suppression, detail preservation, and reconstruction efficiency. In this work, a robust framework which is centered on feature extractor and enhancement is proposed. In the framework, the feature extractor exploits the graph model in latent space for efficient noise suppression and detail preservation, the enhancement collaboratively learns the feature and data statistics by considering the extractor to define regularization. The reconstruction results on the publicly accessible datasets show that the proposed framework outperforms the state-of-art methods considering both quality and efficiency. •The feature extractor suppresses noise and preserves details with efficiency.•The enhancement process collaboratively learns the feature and data statistics.•State-of-the-art performance is achieved for both confocal and non-confocal data.