MEF GAN: Multi exposure Image Fusion via Generative Adversarial Networks
IEEE Transactions on Image Processing(2020)
摘要
In this paper, we present an end-to-end architecture for multi-exposure image fusion based on generative adversarial networks, termed as MEF-GAN. In our architecture, a generator network and a discriminator network are trained simultaneously to form an adversarial relationship. The generator is trained to generate a real-like fused image based on the given source images which is expected to fool the discriminator. Correspondingly, the discriminator is trained to distinguish the generated fused images from the ground truth. The adversarial relationship makes the fused image not limited to the restriction of the content loss. Therefore, the fused images are closer to the ground truth in terms of probability distribution, which can compensate for the insufficiency of single content loss. Moreover, aiming at the problem that the luminance of multi-exposure images varies greatly with spatial location, the self-attention …
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关键词
Gallium nitride, Image fusion, Generative adversarial networks, Generators, Dynamic range, Feature extraction, Training, Image fusion, multi-exposure, generative adversarial network, self-attention
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