基于暗通道和小波的单幅图像烟雾检测算法

Smoke Detection Algorithm of Single Image Based on Dark Channel And Wavelet

针对单幅图像烟雾检测算法的研究较少,而基于烟雾颜色模型的方法易受疑烟区域的干扰等问题,提出了一种多级筛选的单幅图像烟雾检测算法.该算法首先对经过导向滤波优化过的暗通道透射率图做二值化处理,快速去除高透射率干扰区域,得低透射率区域;然后分别对去雾前后的低透射率区域提取YCb Cr颜色空间的Cr通道并差分二值化,去除一些静态背景干扰区域,得疑烟区域;最后分别对去雾前后疑烟区域的Y通道做二维离散小波变换,根据小波能量做差并二值化,得烟雾区域.实验结果表明,本算法能够有效消除图像中的疑烟区域的干扰,准确检测出烟雾区域.

To solve some problems of the single image smoke detection, such as, the study of single image smoke detection is less and smoke detection method of single image based on color model is easily influenced by the color suspected smoke area, we propose a Multilevel filter algorithm of single image smoke detection. Firstly, for the dark channel transmittance chart optimized by the guide filter, the algorithm carries out binaryzation on the image according to certain threshold, which make it fast to remove the interference area of high transmittance and obtain the low transmittance area. Then it removes the smoke in the low transmittance area, extracts the Cr channel of YCb Cr color space before and after smoke removal, subtracts and carries out binaryzation, further removes some static background interference area thereby obtains the suspected smoke area. Finally it extracts the Y channel of YCb Cr color space on the suspected smoke area before and after smoke removal, transforms to two-dimensional discrete wavelet, and makes difference according to the wavelet energy, thereby obtains the final smoke area. Experimental results show that the proposed algorithm can effectively eliminate the interference of the color suspected smoke region in the image, and detect the smoke area accurately.