在有雾气的环境下,户外采集到的图像容易出现对比度低、细节丢失等问题。针对该问题,提出了多尺度Retinex(MSR)理论结合小波变换的图像融合算法对雾天图像进行恢复。首先,用MSR算法对采集到的雾天图像进行增强处理,之后采用‘db5’小波基对雾天图像与增强图像的亮度分量V进行融合处理,并对雾天图像的饱和分量进行约束,最后合成去雾图像。设置阈值,用小波变换对雾浓度相对较大的雾天图像进行二次迭代融合去除残雾。实验结果表明,所提算法可有效恢复不同浓度的雾天图像,去雾后的图像可增强暗区细节、增强图像色彩、丰富图像信息。小波融合的使用保留了更多图像信息,使图像色彩丰富自然、整体平滑,融合图像具有良好的复原效果。

The image collected outdoors in a foggy environment is prone to low contrast and loss of details. To solve this problem, our study proposes an image fusion method based on multiscale Retinex theory and wavelet transform to restore foggy images. First, the MSR algorithm was used to enhance the collected foggy image. Then, the “db5” wavelet basis was used to merge the brightness component V of the foggy image and the enhanced image, and the saturation component of the foggy image was constrained. Finally, the defogging image was generated. The threshold value was set, and the fog image with relatively high fog concentration was second iteratively fused using wavelet transform to remove residual fog. The experimental results show that the proposed algorithm can effectively restore foggy images with different concentrations. Moreover, the image after fog removal can enhance the details of the dark area, improve the image color, and enrich the image information. Using wavelet fusion preserves more image information so that the image color is rich and natural. Thus, the entire smooth fusion image has a good restoration effect.