针对非均匀光照图像存在局部过暗或过亮区域而导致图像对比度低、细节不清晰和可视化效果差的问题，提出了一种基于对称亮度映射和虚拟多曝光融合的图像增强方法。该方法通过颜色空间转换保留输入图像的色度和饱和度分量并分离出亮度分量进行增强。根据相机响应模型，采用图像信息熵和平均梯度最大化原则估计最优曝光比，设计了一种对称亮度映射函数用于虚拟生成对应的最优增强曝光图像和减弱曝光图像，从而与原始亮度分量一起组成具有不同曝光的图像序列，再使用带细节提升的多曝光融合方法对该图像序列重构即得到增强结果。实验结果表明，本文方法在7个公开数据集上的图像信息熵、平均梯度、图像对比度、颜色一致性评价指标均值分别为7.644，9.209，450.683，0.962，均优于对比方法，获得了动态范围高、对比度强、细节清晰和可视化效果好的增强结果。

Aiming at the problems of low contrast, unclear details and poor visualization in non-uniform illumination images due to over-dark or over-bright local regions, an image enhancement method via symmetric brightness mapping and virtual multi-exposure fusion is proposed. A color space conversion is used to retain the hue and saturation components and separate the brightness component of the input image for enhancement processing. According to the camera response model, the principle of image information entropy and average gradient maximization is adopted to estimate the optimal exposure ratio. A pair of symmetrical brightness mapping functions are designed to generate virtually corresponding images with the enhanced exposure and reduced exposure, forming an image sequence with different exposures together with the original brightness component. Then, a multi-exposure fusion method with detail enhancement is applied to the image sequence to reconstruct the enhanced result. Experimental results indicate that the average values of evaluation indices such as image information entropy, average gradient, image contrast, and color consistency of the proposed method are 7.644, 9.209, 450.683 and 0.962 on seven public datasets respectively, which are superior to those of the contrast method and achieve enhanced results with high dynamic range, strong contrast, clear details and good visualization.