Image to Image Translation

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Optimization-based:

  • texture synthesis

    • Texture synthesis using convolutional neural networks. [pdf]

  • feature inversion

    • Understanding Deep Image Representations by Inverting Them.

  • style transfer = feature inversion + texture synthesis

    • Image style transfer using convolutional neural networks. [pdf] [code] (no training, test is slow)

    • Perceptual Losses for Real-Time Style Transfer and Super-Resolution. [pdf] (train a network for each style using style image and content image as inputs, real-time test, belong to one-to-one image mapping)

    • Texture Networks: Feed-forward Synthesis of Textures and Stylized Image. [pdf]

    • A learned representation for artistic style. [pdf] (train a unified network for multiple styles)

Feedforward-based:

  • super-resolution

    • Learning a deep convolutional network for image super-resolution. [pdf]

    • Accurate Image Super-Resolution Using Very Deep Convolutional Networks [pdf] [code] (VGG learns residual)

    • Accelerating the Super-Resolution Convolutional Neural Network. [pdf] (hourglass structure, deconv)

    • Deeply-recursive convolutional network for image super-resolution. [pdf]

    • Photo-realistic single image super-resolution using a generative adversarial network. [pdf] (content_loss, adversarial loss)

  • inpainting or hole-filling

    • Deep Image Inpainting. [pdf]
    • Context Encoders: Feature Learning by Inpainting [pdf] [code]

  • colorization

    • Colorful image colorization. [pdf] [code]

    • Learning Representations for Automatic Colorization. [pdf] [code

  • denoising

    • Image Restoration Using Very Deep Convolutional EncoderDecoder Networks with Symmetric Skip Connections [pdf] [code]:(conv and deconv)

  • decompression

    • Compression Artifacts Reduction by a Deep Convolutional Network [pdf]

  • dehaze/deraining

    • Dehazenet: An end-to-end system for single image haze removal [pdf]

  • demosaicking

    • Deep joint demosaicking and denoising [pdf]

  • image harmonization

  • domain adaptation
    • Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Network. [pdf] [code]

  • general image-to-image translation

    • paired training data

      • Image-to-Image Translation with Conditional Adversarial Nets. [pdf] [code] (pixelGAN)

      • High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs. [pdf]: extend pixel2pixel GAN with coarse-to-fine strategy.

    • unpaired training data

      • Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. [pdf][code] (CycleGAN)

        • DualGAN: Unsupervised Dual Learning for Image-to-Image Translation [pdf]

      • Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. [pdf] (discoGAN)

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