最近新闻
研究领域
Image Auto Line Drawing
Digital drawings blend art appreciation and preservation via edge detection.
Image Intelligent Inpainting
AI inpainting restores damaged art, filling missing parts for image restoration.
Image Sketch Coloring
AI sketch auto-coloring gains attention in computer vision, recognizing and coloring faces, clothes, and background components, enhancing image illustration.
Image Style Transfer
Style transfer: Recompose images in artistic styles, like famous paintings.
Image Super Resolution
Image Super Resolution enhances low-resolution images to high-resolution quality.
In Tibet, innovative thangka designs emerge
最近出版
- Thangka Mural Super-Resolution Based on Nimble Convolution and Overlapping Window Transformer, Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15038. Springer, Singapore
- Image Inpainting of Thangka Murals Using Edge-Assisted Feature Fusion and Self Attention Based Local Refine Network, IEEE Access, 2023
- Thangka Mural Style Transfer Based on Progressive Style-Attentional Network and Multi-Level Loss Function, Journal of Electronic Imaging, 2023
- Thangka Sketch Colorization Based on Multi-Level Adaptive-Instance-Normalized Color Fusion and Skip Connection Attention, Electronics, 2023
- Thangka Mural Line Drawing Based on Dense and Dual-Residual Architecture, Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham.
- Thangka Mural Line Drawing Based on Cross Dense Residual Architecture and Hard Pixel Balancing, IEEE Access, vol. 9, pp. 48841-48850, 2021
- Thanka Mural Inpainting Based on Multi-Scale Adaptive Partial Convolution and Stroke-Like Mask, IEEE Transactions on Image Processing, vol. 30, pp. 3720-3733, 2021
- Damage Sensitive and Original Restoration Driven Thanka Mural Inpainting, Peng Y. et al. (eds) Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science, vol 12305. Springer, Cham.
实验室介绍
The Computer Vision Lab of Northwest Minzu University focuses on artificial intelligence-based computer vision problems. Our main research includes image super-resolution, image style transfer, image auto restoration, image auto coloring, and auto line-extraction of ancient mural images. Our vision is to protect human traditional culture and discover the beauty of the traditional culture using computer science and AI technology.