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CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal

2023-05-09 · Cheng Chen, Shoki Ohta, Takayuki Nishio, Mehdi Bennis, Jihong Park, Mohamed Wahib

Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal processing into the realm of visual scene recovery. Departing from traditional occlusion removal, CSI-Inpainter leverages CSI data to construct and refine obscured visual elements in a scene, facilitating recovery independent of lighting conditions. Validated through comprehensive testing in both office and industrial environments, CSI-Inpainter demonstrates a robust capacity for discerning and reconstructing occluded segments, establishing a new frontier for obstacle removal. This first-of-its-kind framework offers a transformative perspective on how environmental visual information can be extracted from CSI, thereby broadening the scope for computer vision applications in everyday contexts.

📄 PDF Abstract BibTeX arXiv:2305.05385

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Tasks

Image InpaintingImage Restoration

Methods 이 논문이 사용한 방법론

Inpainting Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.

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