Feature separation and non-shadow information-guided shadow removal network
To tackle the performance bottlenecks and color deviation issues stemming from current shadow removal methods, a feature separation and non-shadow information guided shadow removal network (FSNIG-ShadowNet) was constructed. In the separation and reconstruction stage, the shadow image was separated i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024099/ |
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| Summary: | To tackle the performance bottlenecks and color deviation issues stemming from current shadow removal methods, a feature separation and non-shadow information guided shadow removal network (FSNIG-ShadowNet) was constructed. In the separation and reconstruction stage, the shadow image was separated into direct light and ambient light using self-reconstruction supervision, with decoupling of lighting types and reflectance. Subsequently, a decoder was employed to re-couple the separated features to yield shadow-free images. In the refinement stage, the network focused on the adjacent regions of shadow and non-shadow, incorporating a local region adaptive normalization module to transfer the color priors of local non-shadow region to shadow regions for mitigating color deviation between the two regions. Experimental results demonstrate that the proposed FSNIG-ShadowNet achieves competitive results compared to other state-of-the-art methods. |
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| ISSN: | 1000-436X |