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Influence of Color Spaces for Deep Learning Image Colorization

arXiv 2204.02850 ↗First posted 6 Apr 2022cs.CVcited by 0

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Abstract (arXiv)

Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the question: "Is it crucial to correctly choose the right color space in deep-learning based colorization?". First, we briefly summarize the literature and, in particular, deep learning-based methods. We then compare the results obtained with the same deep neural network architecture with RGB, YUV and Lab color spaces. Qualitative and quantitative analysis do not conclude similarly on which color space is better. We then show the importance of carefully designing the architecture and evaluation protocols depending on the types of images that are being processed and their specificities: strong/small contours, few/many objects, recent/archive images.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 94 references that are in the map. First posted on arXiv 6 Apr 2022.

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31 of 94 references.

References · 31

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  4. 2017Mask R-CNNcited by 7,259
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  7. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  8. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  9. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  10. 2016Colorful Image Colorizationcited by 1,113
  11. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  12. 2015A Neural Algorithm of Artistic Stylecited by 480
19 more
  1. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  2. 2019Axial Attention in Multidimensional Transformerscited by 350
  3. 2016Learning Representations for Automatic Colorizationcited by 251
  4. 2019Kornia: an Open Source Differentiable Computer Vision Library for PyTorchcited by 231
  5. 2017Real-Time User-Guided Image Colorization with Learned Deep Priorscited by 123
  6. 2020Bringing Old Photos Back to Lifecited by 95
  7. 2021Colorization Transformercited by 89
  8. 2018Deep Exemplar-based Colorizationcited by 82
  9. 2020Instance-aware Image Colorizationcited by 76
  10. 2016Deep Colorizationcited by 70
  11. 2020HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histogramscited by 64
  12. 2019ChromaGAN: Adversarial Picture Colorization with Semantic Class Distributioncited by 60
  13. 2016Learning Diverse Image Colorizationcited by 43
  14. 2017PixColor: Pixel Recursive Colorizationcited by 32
  15. 2019Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networkscited by 32
  16. 2017Unsupervised Diverse Colorization via Generative Adversarial Networkscited by 30
  17. 2020Time-Travel Rephotographycited by 26
  18. 2018Image Colorization with Generative Adversarial Networkscited by 24
  19. 2017Probabilistic Image Colorizationcited by 13

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