Dong Weida, Wang Chunyan, Sun Hao, Teng Yunjie, Xu Xiping
School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, China.
Sensors (Basel). 2023 Sep 27;23(19):8102. doi: 10.3390/s23198102.
Aiming to solve the problem of color distortion and loss of detail information in most dehazing algorithms, an end-to-end image dehazing network based on multi-scale feature enhancement is proposed. Firstly, the feature extraction enhancement module is used to capture the detailed information of hazy images and expand the receptive field. Secondly, the channel attention mechanism and pixel attention mechanism of the feature fusion enhancement module are used to dynamically adjust the weights of different channels and pixels. Thirdly, the context enhancement module is used to enhance the context semantic information, suppress redundant information, and obtain the haze density image with higher detail. Finally, our method removes haze, preserves image color, and ensures image details. The proposed method achieved a PSNR score of 33.74, SSIM scores of 0.9843 and LPIPS distance of 0.0040 on the SOTS-outdoor dataset. Compared with representative dehazing methods, it demonstrates better dehazing performance and proves the advantages of the proposed method on synthetic hazy images. Combined with dehazing experiments on real hazy images, the results show that our method can effectively improve dehazing performance while preserving more image details and achieving color fidelity.
针对大多数去雾算法中存在的颜色失真和细节信息丢失问题，提出了一种基于多尺度特征增强的端到端图像去雾网络。首先，利用特征提取增强模块捕捉模糊图像的细节信息并扩大感受野。其次，特征融合增强模块的通道注意力机制和像素注意力机制用于动态调整不同通道和像素的权重。第三，上下文增强模块用于增强上下文语义信息，抑制冗余信息，并获得具有更高细节的雾度密度图像。最后，我们的方法去除雾气，保留图像颜色，并确保图像细节。所提方法在SOTS - outdoor数据集上的PSNR得分为33.74，SSIM得分为0.9843，LPIPS距离为0.0040。与代表性的去雾方法相比，它展示了更好的去雾性能，并证明了所提方法在合成模糊图像上的优势。结合对真实模糊图像的去雾实验，结果表明我们的方法能够在保留更多图像细节并实现颜色保真度的同时有效提高去雾性能。
