为了实现外界光照变化或相机积分时间自动调节等情况下拍摄的彩色序列图像的超分辨率重建，文章首先提取图像感兴趣区域的方向梯度直方图作为匹配特征，以相关系数作为相似性测度进行几何配准；其次建立光度测量模型进行光度配准；最后采用最大后验估计算法求出光度改善的高分辨率图像。文章采用平均梯度、信息熵、图像对比度对重建图像质量进行评估。实验表明，重建后高分辨率图像平均梯度及信息熵均有大幅提高，图像更清晰、信息更丰富。算法能有效减小重建图像间的光度差异，提高彩色图像的对比度，对光度变化具有一定的鲁棒性。

In order to realize the super-resolution reconstruction of color sequence images when the external illumination changes or the camera adjusts integration time automatically, an algorithm has been proposed. Firstly, the images’ histograms of oriented gradients (HOG) are extracted as the matching characters and the correlation coefficient is used as the similarity measurements for geometric registration. Secondly, a photometric model is established to achieve photometric registration. Finally, the algorithm of Maximum a Posteriori (MAP) is adopted to get high-resolution images. In order to show the advantage of the algorithm, the results of the average gradient, information entropy and image contrast metric (ICM) of reconstructed images are given. The results show that the average gradient and information entropy of these high-resolution images are enhanced significantly and images have more information. The algorithm is robust to photometric change and it can obviously reduce the photometric difference between the reconstructed images and improve the contrast of color images.