GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks
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ORIGINAL ARTICLE
GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks Zhiguang Yang1 • Youping Chen1
•
Zhuliang Le2 • Yong Ma2
Received: 14 May 2020 / Accepted: 24 September 2020 The Author(s) 2020
Abstract In this paper, a novel multi-exposure image fusion method based on generative adversarial networks (termed as GANFuse) is presented. Conventional multi-exposure image fusion methods improve their fusion performance by designing sophisticated activity-level measurement and fusion rules. However, these methods have a limited success in complex fusion tasks. Inspired by the recent FusionGAN which firstly utilizes generative adversarial networks (GAN) to fuse infrared and visible images and achieves promising performance, we improve its architecture and customize it in the task of extreme exposure image fusion. To be specific, in order to keep content of extreme exposure image pairs in the fused image, we increase the number of discriminators differentiating between fused image and extreme exposure image pairs. While, a generator network is trained to generate fused images. Through the adversarial relationship between generator and discriminators, the fused image will contain more information from extreme exposure image pairs. Thus, this relationship can realize better performance of fusion. In addition, the method we proposed is an end-to-end and unsupervised learning model, which can avoid designing hand-crafted features and does not require a number of ground truth images for training. We conduct qualitative and quantitative experiments on a public dataset, and the experimental result shows that the proposed model demonstrates better fusion ability than existing multi-exposure image fusion methods in both visual effect and evaluation metrics. Keywords Image fusion Multi-exposure image Generative adversarial network
1 Introduction Powered by advanced digital image technology, the effect of image vision is more demanding than ever before. High dynamic range (HDR) technology, the one of the ways to improve image quality, has aroused extensive attention. It is widely applied in the fields of digital electronic products, remote sensing, security monitoring and so on. The dynamic range of image is the ratio of maximum brightness to minimum brightness. The dynamic range of real-world scenes is very wide [1]. However, ordinary image sensors & Youping Chen [email protected] 1
The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
Electronic Information School, Wuhan University, Wuhan 430072, China
have fixed exposure settings and can only get images with low dynamic range (LDR). Thus, due to the limitation of ordinary image sensors, it is difficult for ordinary image sensors to fully present the visual information in the real scene. The HDR technology can improve the dynamic range of the image. Through this technology, the visual in
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