Let there be color!

By Satoshi Iizuka, Edgar Simo-Serra, Hiroshi Ishikawa. 2016

508 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Generative Models

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In Computer Vision and Pattern Recognition

In Media Technology

In Generative Models

Citations in the corpus

Colorful Image Colorization (2016, background): “[24] have developed similar systems, which leverage large-scale data and CNNs.”
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction (2016, background): “A large body of additional work in computer vision and graphics focuses on image channel prediction as an end in itself, such as colorization [47, 26, 20], depth prediction [10], and surface normal prediction [10, 44].”
Scribbler: Controlling Deep Image Synthesis with Sketch and Color (2016, background): “The second and third rows in Figure 3 demonstrate the problems.”
Self-Supervised Generation of Spatial Audio for 360 Video (2018, methodology): “Self-supervised learning Neural networks have been successfully trained through self-supervision for tasks such as image super-resolution [10, 26] and image colorization [19, 44].”
Fast End-to-End Trainable Guided Filter (2018, background): “Such tasks include image denoising [40], image demosaicking [41], image deblurring [42], image matting [43], rain drop removal [44], image dehazing [45], and image colorization [46].”
Deep Video Deblurring for Hand-Held Cameras (2017, methodology): “These methods tend to work best when large training datasets can be easily constructed, for example by adding synthetic noise for denoising [50], removing content for inpainting [35], removing color information for colorization [13], or downscaling for superresolution [7, 27].”
Deep exemplar-based colorization (2018, methodology): “Image colorization is formulated as a regression problem and deep neural networks are used to directly solve it [Cheng et al. 2015; Deshpande et al. 2015; Larsson et al. 2016; Iizuka et al. 2016; Zhang et al. 2016; Isola et al. 2017; Zhang et al. 2017].”
Guided Image Generation with Conditional Invertible Neural Networks (2019, background): “cINN (ours) VAE-MDN [6] cGAN [17] CNN [16] BW Ground truth MSE best of 8 3.”
Faceshop (2018, background): “…proposed for global editing applications such as general image enhancing [Gharbi et al. 2017; Yan et al. 2016], grayscale to color transformation [Iizuka et al. 2016; Zhang et al. 2017], and geometric, illumination, and style transformations [Gatys et al. 2015; Kemelmacher-Shlizerman 2016; Liao…”
Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow (2017, methodology): “2015], visual similarity for product design [Bell and Bala 2015], automatic image colorization [Iizuka et al. 2016] and photo retrieval from sketches [Sangkloy et al.”
Spatially Controllable Image Synthesis with Internal Representation Collaging (2018, methodology): “GANs has been particularly successful on the tasks on images: applications include image colorization [14, 16], inpainting [32, 15, 41], domain translation [16, 40, 46, 37, 39], style transfer [12, 42], object transfiguration [45, 24, 13, 22], just to name a few.”
Deep Image Harmonization (2017, background): “Recently, neural network based methods for image editing tasks such as image colorization [7, 14, 30], inpainting [20] and filtering [18], have drawn much attention due to their efficiency and impressive results.”
ManiGAN: Text-Guided Image Manipulation (2019, background): “Recently, with the development of deep learning and deep generative models, automatic image manipulation has made remarkable progress, including image inpainting [12, 25], image colourisation [40], style transfer [9, 14], and domain or attribute translation [13, 16].”
Semantic photo manipulation with a generative image prior (2019, background): “Recent examples include image inpainting [Iizuka et al. 2017; Pathak et al. 2016; Yu et al. 2018], image colorization [Iizuka et al. 2016; Zhang et al. 2016b], photo stylization [Gatys et al. 2016; Li et al. 2018; Zhu et al. 2017] and photo enhancement [Gharbi et al. 2017; Kim and Park 2018].”
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder (2017, background): “…range of graphics-related tasks, including image classi cation [Krizhevsky et al. 2012], object localization [Girshick et al. 2014], colorization [Iizuka et al. 2016; Larsson et al. 2016], inpainting [Pathak et al. 2016], and view interpolation [Dosovitskiy et al. 2015; Flynn et al. 2016], with…”