After comparing three channels of RGB（Red-green-blue） and three channels of HSV（Hue-saturation-value） in the same scene between clear and fog pictures， a haze removal algorithm based on power exponent stretching is proposed. Firstly， the image is transformed from RGB to HSV space. Then the saturation component and the brightness component are exponentially stretched with power of 1—3，and then they are both adjusted to their suitable range. After stretching transformation of saturation and brightness， the image is transformed from HSV to RGB space to generate enhanced defogging images. Taking the mean value of saturation， brightness index， information entropy and contrast as defog evaluation indexes， the optimal stretching power index combination is determined. The optimal power index combination is used to complete the defogging process. At the same time， it is decided whether to find the optimal power index again according to the change of image average saturation or the length of time interval. Finally， the fog removal algorithm is implemented by multi-process programming with the Python software. When the image resolution is 400 pixel×300 pixel， it takes 5.077—6.160 s to optimize the power index parameters on the raspberry PI. For one frame defogging， the first frame takes longer time of 0.308 s. The other frames take 0.077—0.168 s to removal haze for a single frame.