Volume 39, Number 4, August 2021
|Page(s)||901 - 908|
|Published online||23 September 2021|
A new end-to-end image dehazing algorithm based on residual attention mechanism
School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China
Traditional image dehazing algorithms based on prior knowledge and deep learning rely on the atmospheric scattering model and are easy to cause color distortion and incomplete dehazing. To solve these problems, an end-to-end image dehazing algorithm based on residual attention mechanism is proposed in this paper. The network includes four modules: encoder, multi-scale feature extraction, feature fusion and decoder. The encoder module encodes the input haze image into feature map, which is convenient for subsequent feature extraction and reduces memory consumption; the multi-scale feature extraction module includes residual smoothed dilated convolution module, residual block and efficient channel attention, which can expand the receptive field and extract different scale features by filtering and weighting; the feature fusion module with efficient channel attention adjusts the channel weight dynamically, acquires rich context information and suppresses redundant information so as to enhance the ability to extract haze density image of the network; finally, the encoder module maps the fused feature nonlinearly to obtain the haze density image and then restores the haze free image. The qualitative and quantitative tests based on SOTS test set and natural haze images show good objective and subjective evaluation results. This algorithm improves the problems of color distortion and incomplete dehazing effectively.
Key words: image dehazing / deep learning / channel attention mechanism / residual smoothed dilated convolution / feature extraction
关键字 : 图像去雾 / 深度学习 / 通道注意力机制 / 残差平滑空洞卷积 / 特征提取
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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