Non-homogeneous Haze Removal Based on Attentional Feature Enhancement in Encoder-decoder Networks
Chenyi Wang, Xiaotao Shao, Yan Shen · 2024
Existing image dehazing methods have low quality in dealing with non-uniform haze in real scenes, especially in heavy haze situations. To address this problem, we propose a Multi-Level Feature Extraction Improvement Network (MLFEIN), which effectively removes non-uniform haze in images by extracting and fusing features at different stages and levels. Specifically, we design an Efficient Feature Fusion (EFF) which has an encoder-decoder structure with fusion mechanism, and a Channel and Spatial Information Enhancement (CSIE) module that can efficiently extract features. The EFF module can preserve the image structure information and eliminate artifacts caused by non-uniform haze by combining high-level and low-level features. The CSIE module can further eliminate non-uniform haze by using two separate attention blocks to generate information maps, which utilize spatial and channel information respectively. Moreover, we add residual structures before the attention mechanism to prevent network degradation and enhance global feature extraction. Extensive experiments show that our method achieves better results than the state-of-the-art methods.