Zhao Anxin, Li Liang, Liu Shuai
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
J Imaging. 2024 Jul 11;10(7):164. doi: 10.3390/jimaging10070164.
Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder-decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency features of images, improving the model's performance in image dehazing tasks. Experimental results demonstrate that the proposed model achieves superior dehazing performance compared to existing algorithms on outdoor datasets.
雾霾天气会降低图像质量，导致图像变得模糊且对比度降低。这使得物体边缘和特征不清晰，从而导致检测精度和可靠性降低。为了提高雾霾去除效果，我们提出了一种基于编码器-解码器范式的图像去雾与融合网络（UIDF-Net）。该网络利用图像融合模块（MDL-IFM）融合去雾图像的特征，产生更清晰的结果。此外，为了更好地提取雾霾信息，我们引入了一个雾霾编码器（Mist-Encode），它能有效地处理图像的不同频率特征，提高模型在图像去雾任务中的性能。实验结果表明，与现有算法相比，所提出的模型在室外数据集上具有卓越的去雾性能。
