A GPU-accelerated real-time single image de-hazing method using pixel-level optimal de-hazing criterion
Single image de-hazing is an important and challenging research topic in computer vision. The computational efficiency and robustness of this issue are key problems in real-time applications. In this paper, a graphic processing unit (GPU)-accelerated real-time image enhancing method is proposed to remove haze from a single hazy input image. The foundation of this method is a novel pixel-level optimal de-hazing criterion proposed to combine a virtual hazy-free candidate image sequence into a final single hazy-free image. This image sequence is estimated from the input hazy image by exhausting all possible discretely sampled scene depth values. The main advantage of proposed method is the single pixel independently computing fashion. Its computing at one single pixel position is independent of others. Based on this property, it is straightforward to implement the proposed method with fully parallel GPU acceleration. The sufficient experimentations on various scenes indicate that the proposed method can process a large hazy image with one megapixel to visually moderate haze-free result at a rate of 80frames/s. Moreover, the proposed method is also less affected by the nonuniform illumination compared to previous methods.
image de-hazing optimal de-hazing criterion image fusion real-time gpu computing