Multi-Exposure Image Fusion via Multi-Scale and Context-Aware Feature Learning
Yü Liu, Zhigang Yang, Juan Cheng, Xun Chen
Abstract
In this letter, a deep learning (DL)-based multi-exposure image fusion (MEF) method via multi-scale and context-aware feature learning is proposed, aiming to overcome the defects of existing traditional and DL-based methods. The proposed network is based on an auto-encoder architecture. First, an encoder that combines the convolutional network and Transformer is designed to extract multi-scale features and capture the global contextual information. Then, a multi-scale feature interaction (MSFI) module is devised to enrich the scale diversity of extracted features using cross-scale fusion and Atrous spatial pyramid pooling (ASPP). Finally, a decoder with a nest connection architecture is introduced to reconstruct the fused image. Experimental results show that the proposed method outperforms several representative traditional and DL-based MEF methods in terms of both visual quality and objective assessment.