Image Enhancement with Conditional Adversarial Networks
نویسندگان
چکیده
In this project we try to explore the possibility of using Conditional Adversarial Networks (Conditional GAN) to enhance images. Conditional Adversarial Networks can learn the image-to-image translation and adapt the translation to future images. We try to use Conditional GAN to learn the translation between images from original images and enhanced images and automatically translate original images to the images we want. We compare the difference of purpose and architecture between the Conditional GAN and Cycle-Consistent adversarial networks(Cycle GAN).We use both Conditional GAN and Cycle GAN on training images and the results demonstrates that Conditional GAN are a promising approach for image enhancement.
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