| Presentation | 2023-01-19 [Short Paper] Multimodal Image Inpainting Using Camera Imagery and WiFi RSSI Cheng Chen, Takayuki Nishio, Mehdi Bennis, Jihong Park, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This study demonstrates the feasibility of image inpainting using both visual information and radio frequency (RF) signals. Recent developments in imaging and vision-based technologies using RF signals have revealed the potential of leveraging multimodal information to enhance image inpainting performance. In this context, we propose RF-Inpainter---a novel inpainting method that integrates visual and wireless information by fusing defective RGB images with received signal strength indicator (RSSI) using a deep auto-encoder model. The inpainting performance of RF-Inpainter is evaluated using experimentally obtained images and RSSI datasets in an indoor environment. Image-only inpainting and RSSI-only inpainting models are used as baselines to illustrate the superiority of RF-Inpainter over inpainting methods based on a single modality. The results establish that RF-Inpainter generates satisfactory inpainted images in most experimental scenarios, achieving a maximum improvement of 36.4% and 14.6% in terms of mean peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM), respectively. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Image inpainting / Multi-modal / WiFi sensing / Deep learning / RSSI fingerprint |
| Paper # | SeMI2022-83 |
| Date of Issue | 2023-01-12 (SeMI) |
| Conference Information | |
| Committee | SeMI |
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| Conference Date | 2023/1/19(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Naruto grand hotel |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Koji Yamamoto(Kyoto Univ.) |
| Vice Chair | Kazuya Monden(Hitachi) / Yasunori Owada(NICT) / Shunsuke Saruwatari(Osaka Univ.) |
| Secretary | Kazuya Monden(NTT DOCOMO) / Yasunori Owada(Tokyo Univ. of Agri. and Tech.) / Shunsuke Saruwatari(Osaka Univ.) |
| Assistant | Yuki Matsuda(NAIST) / Akihito Taya(Aoyama Gakuin Univ.) / Takeshi Hirai(Osaka Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Sensor Network and Mobile Intelligence |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Short Paper] Multimodal Image Inpainting Using Camera Imagery and WiFi RSSI |
| Sub Title (in English) | |
| Keyword(1) | Image inpainting |
| Keyword(2) | Multi-modal |
| Keyword(3) | WiFi sensing |
| Keyword(4) | Deep learning |
| Keyword(5) | RSSI fingerprint |
| 1st Author's Name | Cheng Chen |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 3rd Author's Name | Mehdi Bennis |
| 3rd Author's Affiliation | University of Oulu(Oulu Univ.) |
| 4th Author's Name | Jihong Park |
| 4th Author's Affiliation | Deakin University(Deakin Univ.) |
| Date | 2023-01-19 |
| Paper # | SeMI2022-83 |
| Volume (vol) | vol.122 |
| Number (no) | SeMI-341 |
| Page | pp.pp.51-53(SeMI), |
| #Pages | 3 |
| Date of Issue | 2023-01-12 (SeMI) |