Dust Image Enhancement Algorithm Based on Color Transfer

The increasing dust weather has seriously affected the quality of captured images. Therefore, research of the dust image quality enhancement has become an important hotspot in the field of computer vision. Compared with pictures which obtained in sunny day, dust images have some obvious problems such as low-quality in definition and light, hue yellowing and so on. Amid such questions, available methods cannot always prevail. Hence, this paper proposed a dust image enhancement algorithm based on color transfer. First of all, applying the scene gist feature to select a target image which has the highest similarity with input image in clear images database; then, the target image color information is passed to input image through the color transfer algorithm; finally, utilizing the contrast limited adaptive histgram equalization algorithm to restore the definition in dust image. The experimental results show that our algorithm can effectively enhance dust images quality in different degree, get more image details and resolve the definition and hue correction problems. We proposed a absolute color difference index to measure this method. The experiments demonstrate that the proposed algorithm outperforms the state-of-the-art models.