古代壁画是世界范围内的珍贵文化遗产，其独特的制作方式，鲜明的绘画风格和生动的绘画内容都极具保存和研究价值。但由于长期受环境、人为因素的影响，古壁画都面临不同程度病害问题，而且缺乏有效的非侵入式监测手段。本项目基于高光谱图像分析技术对古壁画病害问题展开深入研究，包括古壁画病害自动识别和量化问题，基于微观致病成分的病害机理研究和预测问题，基于高光谱壁画图像艺术风格分析的数字化恢复问题。为了解决这些有挑战性的问题，该项目将着重研究高光谱数据的降噪预处理、稀疏表示、基于2D奇异谱分析的特征提取与降维，非线性解混模型，基于深度学习和主动学习的病害预测及风格提取模型等算法，从而实现自动、准确和高效的壁画病害量化监测、致病成分分析及预警和高质量的风格化数字恢复的理论及方法研究。

As precious cultural heritage worldwide, historical frescos, have extremely high preservation and research values in terms of their unique way of making, distinctive painting style and vivid painting contents. However, due to the longstanding effects in terms of environmental changes, physical impacts of human, frescos are commonly faced with various degrees of diseases, and there is no effective non-invasive monitoring means for the frescos. This project is a hyperspectral imaging analysis based in-depth study for diseased problem of frescos, including automatic identification and quantification of fresco disease problems, the problem of microcosmic component based frescos disease intrinsic mechanism and predicting，and hyperspectral imaging based stylized recovery of frescos. To solve the aforementioned challenging problems, the proposed project will focus on algorithms of denoise and preprocessing of hyperspectral data, sparse representation, 2D-SSA (singular spectral analysis) based feature extraction and data reduction, nonlinear unmixing model, deep-learning and active-learning based prediction and decision making, and style extraction model. So that we will achieve series of methods about automatic, accurate and effective quantity monitoring of frescos, pathogenic component analysis and early warning, and stylized restoration with high quality.