林业机器人在林区行走作业时的滑动、越障时的抖动和机械臂的振动，造成其视觉系统获取的活立木彩色图像产生运动模糊，直接影响林业机器人对活立木的识别、三维重建和测量；同时林区自然光照条件复杂多变，导致活立木运动模糊图像复原困难。针对这一科学问题，本申请尝试从活立木图像颜色恒常性入手，首先通过典型林区不同季节不同光照条件下的活立木图像建立具有颜色不变性的颜色空间；根据不变矩理论在所建立的颜色空间下构建活立木图像的颜色不变性描述子；以颜色不变性描述子为字典构建活立木图像的稀疏表达，通过正则化手段建立一种基于颜色恒常性和字典迁移的稀疏正则化运动模糊图像复原方法，解决活运动模糊立木彩色图像复原这一病态盲复原问题，为自主作业林业机器人的活立木三维重建、作业目标识别和作业环境建模奠定理论基础与提供技术支撑，促进自主作业林业机器人的发展。

Forestry robots may slide while working, jitter while crossing obstacles and encouter vibration of manipulators, which will cause motion blur in acquiring color images of standing trees, directly affect the recognition, 3D reconstruction and measurement of standing trees. Meanwhile, it’s hard to restore motion blurring images of standing trees due to the complex forest natural light conditions. The project attempts to solve the scientific problem from the point of image color constancy of standing trees. The color space with color invariance should be established based on the images of standing trees in different seasons and different light conditions. The color invariant descriptors should be built in the eatablished color space based on moment invariant theory. Sparse image expression should be constructed using moment theory color invariant descriptor as dictionary. By regularization, a method of restoring sparse regularization motion blurring images can be proposed based on color constancy and dictionary migration, which can solve the ill-posed blind restoration problem of restoring motion blurring images of standing trees, establish theoretical foundation for 3D reconstruction of standing trees, the target recognition and work-environment modeling for autonomous forestry robots and promote the development of autonomous forestry robots.