Si Yazhong, Yang Fan, Liu Zhao
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China.
Sci Rep. 2022 Aug 2;12(1):13226. doi: 10.1038/s41598-022-17530-3.
The outdoor images captured in sand dust weather often suffer from poor contrast and color distortion, which seriously interfere with the performance of intelligent information processing systems. To solve the issues, a novel enhancement algorithm based on fusion strategy is proposed in this paper. It includes two components in sequence: sand removal via the improved Gaussian model-based color correction algorithm and dust elimination using the residual-based convolutional neural network (CNN). Theoretical analysis and experimental results show that compared with the prior sand dust image enhancement methods, the proposed fusion strategy can effectively correct the overall yellowing hue and remove the dust haze disturbance, which provides a constructive idea for the future development of sand dust image enhancement.
沙尘天气下拍摄的室外图像往往对比度差、颜色失真，严重干扰智能信息处理系统的性能。为解决这些问题，本文提出了一种基于融合策略的新型增强算法。它依次包括两个部分：通过改进的基于高斯模型的颜色校正算法去除沙尘，以及使用基于残差的卷积神经网络（CNN）消除灰尘。理论分析和实验结果表明，与现有的沙尘图像增强方法相比，所提出的融合策略能够有效校正整体泛黄色调并去除沙尘雾霾干扰，为沙尘图像增强的未来发展提供了建设性思路。
