A fast hardware accelerator for nighttime fog removal based on image fusion

In this paper, a fast hardware accelerator for defogging based on image fusion is proposed. This method overcomes the problem of model based defogging algorithms being unable to estimate atmospheric light in dark scenes, as well as the poor performance of learning based defogging algorithms at night. Through hardware implementation and optimization, while reducing system resources, it can meet the demand for real-time defogging. The entire algorithm consists of difference guided filtering, grayscale linear stretching, and image fusion. The difference oriented filtering algorithm can enhance edges by obtaining image information of bright and dark channels, and has better effects on night lighting. Gray-scale linear stretching can restore the overall brightness and edge information of the image, compensating for some halos and noise caused by difference guided filtering. Numerous experiments have shown that the proposed hardware accelerator for defogging performs best at night. It can also be used effectively during the day. In addition, it has the fastest processing speed, which can process the images with the size of 1920*1080 for 34.5fps in real time.