为解决低光照环境下机器视觉采集图像存在的对比度低、细节丢失等问题，提出了一种基于扩展大气散射模型的低光照图像增强算法。首先，将低光照图像三个颜色通道的最大值作为初始传输图，并使用伽马校正调整图像的可见性和边缘细节；接着，利用融合技术优化初始传输图，融合不同方法提取的主要结构和精细结构；然后，利用亮通道的暗像素计算逆大气光值；最后，根据传输图和逆大气光值对模型进行求解得到最终增强图像。模型中的校正项可以更好地抑制增强图像的过度增强与细节丢失，同时，算法采用图像融合对传输图进行优化，可以较好地再现图像中的轮廓和纹理细节。实验结果表明，相比于其他8种算法，该算法在提高图像对比度、自然度和突出细节方面表现出了更好的性能。

Low contrast and weak detail features of images collected in a low-light environment will seriously affect the accuracy and stability of machine vision detection. In recent years, the low-light image enhancement technology has made remarkable progress. However, the existing low-light image enhancement algorithms have some problems, such as image detail loss, low brightness, local exposure, insufficient visual naturalness, complex algorithm and high resource overhead. To solve the above problems, a low-light image enhancement algorithm based on extended atmospheric scattering model is proposed. Firstly, the maximum value of R, G and B color channels is calculated and the initial transmission map is obtained by gamma correction. Secondly, the main structure and fine structure of the initial transmission map were extracted, PCA (Principal Component Analysis) method was used to fuse the main structure transmission map and fine structure transmission map to obtain the optimized local consistency transmission map of texture detail removal. Then, the inverse atmospheric light value is calculated using the dark pixel of the bright channel. Finally, the LIEAS (Low-light Image Enhancement via Extend Atmospheric Scattering Model) model is solved to obtain the final enhanced image with natural color and good contrast. The enhanced model derived by the algorithm is similar to the Retinex enhanced model in form, but the difference is that there is an additional correction term in the LIEAS model, which can better suppress the excessive enhancement and detail loss in the image. The algorithm uses the image fusion method to optimize the transmission image and can reproduce the contour and texture details well. In order to evaluate the algorithm objectively, spatial frequency, average gradient, edge intensity and natural image quality evaluation are used as the image quality evaluation metrics. In order to verify the effectiveness of the algorithm, the parameter analysis experiment, model analysis experiment and performance comparison experiment are carried out respectively. In the parameter analysis experiment, firstly, the influence of the selection of gamma parameters on the enhanced image is analyzed. The subjective visual analysis and objective data analysis are carried out on the test results under different parameters, and a good gamma parameter value is obtained. Secondly, the influence of the selection of the maximum filtering window size of the bright channel on the solution of the inverse atmospheric light value and the enhanced image is analyzed. The test results under different window sizes are analyzed to obtain an appropriate window size. Then, the darkest pixel proportion of the bright channel in the solution of the inverse atmospheric light value is selected and analyzed. Finally, this paper verifies the advantages of the transmission map optimization method based on fusion technology. In the model analysis experiment, compared with Retinex model, spatial frequency and average gradient of the proposed algorithm are significantly improved, which also has prominent visual advantages, indicating that the correction term in the proposed algorithm can better suppress the excessive enhancement and detail loss of the enhanced image, and has good enhancement ability. At the same time, by changing the atmospheric light value in the model, the proposed algorithm can also be used in image dehazing, and the image dehazing can get a good effect from both subjective and objective aspects. In the performance verification experiment, three low-light image datasets were selected to test, and the performance of the proposed enhancement algorithm was compared with that of other eight algorithms from both subjective and objective aspects. Compared with the other eight algorithms, this algorithm has the advantages of bright background, high contrast, complete edge details, natural, vivid image, avoiding local overexposure and so on. The algorithm has more advantages in spatial frequency, average gradient, and edge intensity, which indicates that the algorithm has better performance in the aspects of image color richness and image sharpness. Both for the metric analysis of the whole image of the dataset and for the metric analysis of a single image, the proposed algorithm is very advantageous, and the model is simple and low complexity. Compared with the existing enhancement algorithms, the proposed algorithm has some advantages in detail information retention, contrast enhancement, image naturalness and local overexposure suppression.