Attention-Guided Global-Local Adversarial Learning for Detail-Preserving Multi-Exposure Image Fusion

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 8; pp. 5026 - 5040
• Main Authors: Liu, Jinyuan, Shang, Jingjie, Liu, Risheng, Fan, Xin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; adversarial learning; attention learning; Color; Computer vision; Deep learning; Distortion; Exposure; Feature extraction; illumination correction; Image color analysis; Image edge detection; Image fusion; Image processing; Image restoration; Inspection; multi-exposure image; Source code; Task analysis
• DOI: 10.1109/TCSVT.2022.3144455
• ISSN: 1051-8215

Deep learning networks have recently demonstrated yielded impressive progress for multi-exposure image fusion. However, how to restore realistic texture details while correcting color distortion is still a challenging problem to be solved. To alleviate the aforementioned issues, in this paper, we propose an attention-guided global-local adversarial learning network for fusing extreme exposure images in a coarse-to-fine manner. Firstly, the coarse fusion result is generated under the guidance of attention weight maps, which acquires the essential region of interest from both sides. Secondly, we formulate an edge loss function, along with a spatial feature transform layer, for refining the fusion process. So that it can take full use of the edge information to deal with blurry edges. Moreover, by incorporating global-local learning, our method can balance pixel intensity distribution and correct the color distortion on spatially varying source images from both image/patch perspectives. Such a global-local discriminator ensures all the local patches of the fused images align with realistic normal-exposure ones. Extensive experimental results on two publicly available datasets show that our method drastically outperforms state-of-the-art methods in visual inspection and objective analysis. Furthermore, sufficient ablation experiments prove that our method has significant advantages in generating high-quality fused results with appealing details, clear targets, and faithful color. Source code will be available at https://github.com/JinyuanLiu-CV/AGAL .