Enhance Images as You Like with Unpaired Learning

• URL: http://arxiv.org/abs/2110.01161v1
• Date: Mon, 4 Oct 2021 03:00:44 GMT
• Title: Enhance Images as You Like with Unpaired Learning
• Authors: Xiaopeng Sun, Muxingzi Li, Tianyu He, Lubin Fan
• Abstract summary: We propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space. Our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets.
• Score: 8.104571453311442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.

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