灰度拉伸Retinex 在大动态范围烟雾图像增强中的应用

为解决Retinex 算法不能有效增强大动态范围烟雾干扰图像的问题，分析了影响其增强效果的原因并提出了一种自适应灰度拉伸Retinex 算法。该算法建立了烟雾区域灰度估计数学模型，通过计算图像的局部动态范围和信息熵，自适应地估计烟雾干扰区域的灰度范围并进行灰度拉伸，采用不同尺度的Retinex 对各区域进行处理，得到最终的增强图像。实验表明，该算法能够在全局动态范围较大时增加图像的信息熵，对低对比度烟雾干扰区域有明显的增强效果。

Application of Retinex with grayscale stretching in large dynamic range smoke image enhancement

To solve the problem that traditional Retinex cannot work well in large dynamic range smoke image enhancement, the reasons was analyzed and a new Retinex algorithm was proposed with self- adaptive grayscale stretching. A mathematical model was built to estimate the gray level range of smoke area by calculating local dynamic range and information entropy. By stretching the gray level range calculated and processing the image with Retinex of different scales, the enhanced image was got. Experiment shows that the method can increase the information entropy of large dynamic range image and enhance the contrast of smoke area.