Significant research has been conducted in the field of image haze removal locally and internationally. However

haze removal methods

which have good effects

often take a long time. Moreover

effects of fast methods are general to reach the requirements of applications in many occasions. This study aims to present a method that has fast processing speed and improved effect based on mainstream haze removal methods. The haze removal method via dark channel that is previously suggested by He is simple and effective. However

it keeps residual haze near depth edges after haze removal. Moreover

it leads to color distortion in sky area and large white areas

which do not previously meet dark channel. In addition

its processing speed is slow. This study aims to solve these problems and to present a fast and effective method based on the method suggested by He. The appearance of residual haze is due to block thought adoption via the method of He and the assumption that the transmission keeps unchanged in a local patch. We can abandon block thought

cancel the minimum filtering operation

and use per-pixel processing method to estimate transmission map. The appearance of color distortion is due to extremely low estimation by the method of He for the transmission in the sky area and large white areas

which leads to subtle differences among pixel RGB color channels in these regions

which are magnified nearly 10 times. Thus

we can increase transmission of these regions properly. Atmospheric light can be estimated with quadtree algorithm

which is efficient. Our method has solved the problems of residual haze and color distortion effectively. Moreover

haze removal speed is enhanced greatly because operations of minimum filtering and soft matting or guided filtering in the process of estimating the transmission map are abandoned and the atmospheric light is improved by solving efficiency. The speed of our method is about four times of that of the method by He. Experimental results show that our algorithm can greatly improve efficiency of haze removal and can save the time spend by haze removal under premise of good effect. Our method can keep good effect of haze removal for most haze images. However

if an image has deep scene depth

our method have a general manifestation on its distance scene. On account of the advantage in speed

our method is suitable for real-time demand higher occasions.