现有图像去雾算法在复原含明亮区域的雾天图像时，存在色彩失真、偏色、亮度低等问题。针对现有算法的不足，提出一种基于指数映射与自适应权重能量函数的单幅图像去雾算法。首先结合图像暗通道值的统计规律，利用指数函数衰减特性，构建清晰图像暗通道与有雾图像暗通道的指数映射模型，并根据所获得的暗通道估计值求解出透射率估计值；其次，根据图像的马尔可夫性，构建基于马尔可夫网的自适应权重能量函数，对透射率进行优化，并使用降采样方法降低算法复杂度；最后，利用优化后的透射率估计值与局部大气光值复原出无雾图像。实验对比结果表明，该算法复原结果视觉效果清晰、色彩保真度高，并且多项客观评价参数在实验对比中取得了最高值，其中直方图相关系数达到了0.4521，高出对比算法的平均表现67.3%。综上所述，该算法较好地解决了包含明亮区域的有雾图像复原问题。

Existing image dehazing algorithms have problems in restoring hazy images with bright areas, such as color distortion, color offset, and brightness reduction. Aiming at the shortcomings of existing methods, a single image dehazing algorithm is proposed based on exponential mapping and adaptive weight energy function. Firstly, according to statistical law of the dark channel prior, the attenuation characteristics of the exponential function are utilized to construct a dark channel mapping model between clear image and hazy image. Subsequentially, the estimated value of the transmission can be calculated based on that of obtained dark channel. Secondly, according to the Markov property of images, a Markov network-based adaptive weight energy function is constructed to optimize the transmission and the down-sampling method is used to reduce the algorithm complexity. Finally, the haze-free image is restored by using the optimized transmission and local atmospheric light. The experimental results show that the restored images of the proposed algorithm have clear visual effects and high color fidelity. And several objective evaluation parameters reach the highest values. The histogram correlation coefficient reaches 0.4521, which is 67.3% higher than those of the average performance of comparison algorithms. In summary, the proposed algorithm can effectively solve the recovery problems of hazy image with bright areas.