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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2405.10504 |
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| _version_ | 1866929504477249536 |
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| author | Zeng, Jianshun Li, Wang Lv, Yanjie Gao, Shuai Qin, YuChu |
| author_facet | Zeng, Jianshun Li, Wang Lv, Yanjie Gao, Shuai Qin, YuChu |
| contents | Street-view image has been widely applied as a crucial mobile mapping data source. The inpainting of street-view images is a critical step for street-view image processing, not only for the privacy protection, but also for the urban environment mapping applications. This paper presents a novel Deep Neural Network (DNN), multi-scale semantic prior Feature guided image inpainting Network (MFN) for inpainting street-view images, which generate static street-view images without moving objects (e.g., pedestrians, vehicles). To enhance global context understanding, a semantic prior prompter is introduced to learn rich semantic priors from large pre-trained model. We design the prompter by stacking multiple Semantic Pyramid Aggregation (SPA) modules, capturing a broad range of visual feature patterns. A semantic-enhanced image generator with a decoder is proposed that incorporates a novel cascaded Learnable Prior Transferring (LPT) module at each scale level. For each decoder block, an attention transfer mechanism is applied to capture long-term dependencies, and the semantic prior features are fused with the image features to restore plausible structure in an adaptive manner. Additionally, a background-aware data processing scheme is adopted to prevent the generation of hallucinated objects within holes. Experiments on Apolloscapes and Cityscapes datasets demonstrate better performance than state-of-the-art methods, with MAE, and LPIPS showing improvements of about 9.5% and 41.07% respectively. Visual comparison survey among multi-group person is also conducted to provide performance evaluation, and the results suggest that the proposed MFN offers a promising solution for privacy protection and generate more reliable scene for urban applications with street-view images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10504 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image Zeng, Jianshun Li, Wang Lv, Yanjie Gao, Shuai Qin, YuChu Computer Vision and Pattern Recognition Street-view image has been widely applied as a crucial mobile mapping data source. The inpainting of street-view images is a critical step for street-view image processing, not only for the privacy protection, but also for the urban environment mapping applications. This paper presents a novel Deep Neural Network (DNN), multi-scale semantic prior Feature guided image inpainting Network (MFN) for inpainting street-view images, which generate static street-view images without moving objects (e.g., pedestrians, vehicles). To enhance global context understanding, a semantic prior prompter is introduced to learn rich semantic priors from large pre-trained model. We design the prompter by stacking multiple Semantic Pyramid Aggregation (SPA) modules, capturing a broad range of visual feature patterns. A semantic-enhanced image generator with a decoder is proposed that incorporates a novel cascaded Learnable Prior Transferring (LPT) module at each scale level. For each decoder block, an attention transfer mechanism is applied to capture long-term dependencies, and the semantic prior features are fused with the image features to restore plausible structure in an adaptive manner. Additionally, a background-aware data processing scheme is adopted to prevent the generation of hallucinated objects within holes. Experiments on Apolloscapes and Cityscapes datasets demonstrate better performance than state-of-the-art methods, with MAE, and LPIPS showing improvements of about 9.5% and 41.07% respectively. Visual comparison survey among multi-group person is also conducted to provide performance evaluation, and the results suggest that the proposed MFN offers a promising solution for privacy protection and generate more reliable scene for urban applications with street-view images. |
| title | Multi-scale Semantic Prior Features Guided Deep Neural Network for Urban Street-view Image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.10504 |