Summary of Progressive Enhancement and Restoration For Mural Images Under Low-light and Defected Conditions Based on Multi-receptive Field Strategy, by Xiameng Wei et al.
Progressive enhancement and restoration for mural images under low-light and defected conditions based on multi-receptive field strategy
by Xiameng Wei, Binbin Fan, Ying Wang, Yanxiang Feng, Laiyi Fu
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a two-stage restoration model, called MER (Mural Enhancement and Restoration net), to address the damage and degradation of ancient murals. The proposed model aims to enhance the visual quality of restored images while achieving commendable results in metric evaluations compared to competitors. The authors propose an automatic defect area detection strategy to facilitate batch restoration at archaeological sites. The model is tested on damaged ancient murals captured in low light, which hampers further restoration efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us fix and make better old paintings on walls that are important for learning about history and culture. Old paintings get damaged over time because of things like mold and bad lighting. To help with this, the authors suggest a special way to fix these paintings called MER (Mural Enhancement and Restoration net). It can even do multiple paintings at once! They tested it on some old paintings that were hard to see because they were taken in low light. |
