颜色恒常性是人类视觉系统实现颜色感知的重要条件之一，模拟生物视觉颜色信息处理机制建立具有自适应性的图像颜色校正算法已成为机器视觉领域的研究热点。本项目在我们前期研究工作的基础上，充分吸收国际上在视觉机理实验研究方面的长期积累和最新进展，从视觉颜色信息处理、自然图像统计、神经元中心-外周感受野非线性信息处理三个层次系统性地模拟视觉自适应生理机制，建立能应用于复杂场景下的自适应多光源颜色恒常性模型。使得所建立的模型具有如下特点：1)模型所采用的机制符合神经元自适应信息处理的基本实验结论；2）能够解释电生理实验上观测到的各种神经元自适应现象；3）能够自适应地处理复杂非均匀光照条件下的色偏图像。本项目的研究成果将为自适应图像颜色校正这一计算机视觉中的基本问题提供有效解决方案，为基于颜色视觉的场景分析任务（如目标识别）提供最为直接的支持，进而为推进计算机视觉系统的生物智能化做出贡献。

Color constancy is one of the important conditions of human visual system for color perception, and the modeling of adaptive color processing of biological visual system is also a hot topic in the field of computer vision. In this proposed project, we plan to study and simulate the vision adaptive mechanisms from the perspectives of visual color information processing, the natural image statistics and the nonlinear center-surround interactions of neurons, based on the extensive known knowledge and the various newest findings about the biological vision. The final goal is to build multi-light sources color constancy algorithms based on vision adaptive mechanism in complex scene that have the following features: 1) the introduced adaptive frameworks are as close as to the neuronal ones; 2) the proposed models can explain certain neurophysiological effects of visual adaptation; 3) the new models can be used to adaptively correct the color-biased scenes under varying external lighting conditions. Ultimately, this project may provide adaptive solutions to the principle problem of color correction in the field of computer vision, and hence, benefits directly the color information based applications, e.g., color based object recognition. To the end, the project may contribute to advance the computer vision systems more biologically intelligent.