利用色彩直方图特征进行偏色图象的自动检测和校正

在肤色检测、人脸识别、图象和视频检索的研究中，大量算法都是基于对图象色彩特征进行分析的，然而当图象发生偏色时，这些算法的性能会明显下降，甚至无效，而且由于现有的偏色校正算法，引入了其他关于偏色图象的先验性信息，具有很大的使用局限性，为此，提出了一种在只给出偏色图象的条件下，进行偏色检测和自动校正的算法．该算法首先获取并分析偏色图象在RGB各通道内的直方图特征，然后参照这些特征检测偏色通道，并通过调整偏色或非偏色通道强度分布来达到各个通道之间色彩平衡．实验表明，在较大程度的偏色情况下，该算法校正恢复出的图象与原始无偏色图象能达到视觉上基本一致的效果，并具有普遍的适用性．

Automatic Illuminations Detection and Color Correction of Image Using Chromatic Histogram Characters

Color is used as most an important cue in those studies such as skin detection, face detection and recognition, image and digital video retrieval. But under changing illumination, objects may show different color as the original one it is, especially the lights run out of the range of the camera sensors. In such worse condition, many algorithms become useless. In order to solve this problem, many researchers introduced additional known knowledge about the objects or environment and so have their use limitation. In this paper, an algorithm's architecture is proposed to automatically detect illumination and correct the color of image without use of any additional knowledge and any assumed ideal condition, but the image itself. By analysing the chromatic histogram characters of uncorrected color image in RGB channels, it first detect which channel cause color uncorrected and then use histogram characters above to adjust channel's intension until it reach balance between each channel. A large of different uncorrected color images is used to verify the efficiency of our algorithm, including computer simulating images, face images under different illuminants and images in art and films. The result shows that our algorithm works well in worse condition, and can correct and restore the image to its original color in most circumstance.