针对在雾霾天气、水下和夜间环境中获得的图像存在清晰度和对比度下降以及色彩失真等问题，提出一种使用改进型大气散射模型的双阶段图像修复方法。通过在传统的大气散射模型中引入一个全局补偿系数以得到一个改进型大气散射模型，使用该模型的双阶段图像修复方法包含两个阶段：首先，输入一张退化图像，利用改进型大气散射模型得出一张粗略的修复图像，并利用灰度世界算法求出该粗略的修复图像的反照率；其次，将反照率和第一阶段的输出图像作为输入，利用改进型大气散射模型得出最终的修复图像。实验结果表明，所提出的方法可避免图像修复的结果中存在色彩失真和色调偏暗等问题，并且具有很好的适用性，其不仅可有效实现雾霾图像去雾，也可实现水下图像修复和夜间图像增强。与当前最先进的方法相比，所提出的方法在定量和定性实验上都取得了优异的结果。

Targeting negative effects such as clarity and contrast degradation and color distortion of images acquired in hazy weather， underwater， and in nighttime environments， a two-stage image restoration method using an improved atmospheric scattering model is proposed. A global compensation coefficient is introduced into the traditional atmospheric scattering model to obtain an improved atmospheric scattering model； the two-stage image restoration method based on this model consists of two stages. First， a degraded image is fed to the improved atmospheric scattering model to obtain a coarse restored image. The grayscale world algorithm is then used to determine the albedo of this coarse restored image. Second， the albedo and output image of the first stage are fed to the improved atmospheric scattering model to obtain the final restored image. Experimental results indicate that the proposed method can avoid the problems of color distortion and dark tones in the restored images and has good applicability. The method can effectively achieve image dehazing， underwater image restoration， and night image enhancement. The proposed method achieves excellent results in both quantitative and qualitative experiments compared with state-of-the-art methods.