关键词: 去雾算法, 卷积网络, 计算机视觉, 深度学习

Abstract:

Aiming at the limitation of the current dehazing technology and the slow processing speed, a lightweight dehazing algorithm based on multi-scale convolutional network is proposed. The algorithm has two parts：dehazing and repairing. The dehazing module accepts the foggy image. After feature extraction and fusion, the transmittance map and the atmospheric light value are uniformly learned. And the deformed atmospheric physical scattering algorithm is used to invert the fog-free image. But the fog-free image still has such problems as the dark hue and the unclear details. The repairing module repairs the image from the dehazing module by limiting the contrast histogram equalization（CLAHE） to improve the image contrast and the robustness of the algorithm. In the experiments, the dehazing task is combined with the target detection task to verify the validity of the algorithm.

Key words: dehazing algorithm, convolutional network, computer vision, deep learning