Skip to main content
Register
Springer Professional
SEARCH
Menu 1 2 3 4
Top

Hint

Swipe to navigate through the chapters of this book

Published in:
Improved Kernel Density Estimation Self-organizing Incremental Neural Network to Perform Big Data Analysis Read chapter Read first chapter

2018 | OriginalPaper | Chapter

CocoNet: A Deep Neural Network for Mapping Pixel Coordinates to Color Values

Authors : Paul Andrei Bricman, Radu Tudor Ionescu

Published in: Neural Information Processing

Publisher: Springer International Publishing

Login to get access
share
SHARE

Abstract

We propose a deep neural network for mapping the 2D pixel coordinates in an image to the corresponding RGB color values. The neural network is termed CocoNet, i.e. coordinates-to-color network. During the training process, the neural network learns to encode the input image within its layers, i.e. it learns a continuous function that approximates the discrete RGB values sampled over the discrete 2D pixel locations. At test time, given a 2D pixel coordinate, the neural network will output the RGB values of the corresponding pixel. By considering every 2D pixel location, the network can actually reconstruct the entire learned image. We note that we have to train an individual neural network for each input image, i.e. one network encodes a single image. Our neural image encoding approach has various low-level image processing applications ranging from image denoising to image resampling and image completion. Our code is available at https://​github.​com/​paubric/​python-fuse-coconet.
previous chapter Hashtag Recommendation with Attention-Based Neural Image Hashtagging Network
next chapter BCMLP: Binary-Connected Multilayer Perceptrons
Literature
1.
Baig, M.H., Koltun, V., Torresani, L.: Learning to inpaint for image compression. In: Proceedings of NIPS, pp. 1246–1255 (2017)
2.
Ballé, J., Laparra, V., Simoncelli, E.P.: End-to-end optimized image compression. In: Proceedings of ICLR (2017)
3.
Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of BMVC, pp. 135.1–135.10 (2012)
4.
Cavigelli, L., Hager, P., Benini, L.: CAS-CNN: A deep convolutional neural network for image compression artifact suppression. In: Proceedings of IJCNN, pp. 752–759 (2017)
5.
6.
Dumas, T., Roumy, A., Guillemot, C.: Image compression with stochastic winner-take-all auto-encoder. In: Proceedings of ICASSP, pp. 1512–1516 (2017)
7.
Girshick, R.: Fast R-CNN. In: Proceedings of ICCV, pp. 1440–1448 (2015)
8.
9.
10.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR, pp. 770–778, June 2016
11.
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: Stacked generative adversarial networks. In: Proceedings of CVPR, pp. 5077–5086 (2017)
12.
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 107 (2017) CrossRef
13.
Ionescu, R.T., Alexe, B., Leordeanu, M., Popescu, M., Papadopoulos, D., Ferrari, V.: How hard can it be? Estimating the difficulty of visual search in an image. In: Proceedings of CVPR, pp. 2157–2166, June 2016
14.
Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-recursive convolutional network for image super-resolution. In: Proceedings of CVPR, pp. 1637–1645 (2016)
15.
Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. Technical report, University of Toronto (2009)
16.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Bartlett, P., Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Proceedings of NIPS, pp. 1106–1114 (2012)
17.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998) CrossRef
18.
Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of CVPR, pp. 4681–4690 (2016)
19.
Liu, W., et al.: SSD: single shot MultiBox detector. In: Proceedings of ECCV, pp. 21–37 (2016) CrossRef
20.
Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Proceedings of NIPS, pp. 2802–2810 (2016)
21.
Minnen, D., et al.: Spatially adaptive image compression using a tiled deep network. In: Proceedings of ICIP, pp. 2796–2800 (2017)
22.
23.
Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: Proceedings of BMVC, pp. 6–17 (2015)
24.
Prakash, A., Moran, N., Garber, S., DiLillo, A., Storer, J.: Semantic perceptual image compression using deep convolution networks. In: Proceedings of DCC, pp. 250–259 (2017)
25.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of CVPR, pp. 779–788 (2016)
26.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of NIPS, pp. 91–99 (2015)
27.
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015) MathSciNetCrossRef
28.
Sajjadi, M.S., Schölkopf, B., Hirsch, M.: Enhancenet: single image super-resolution through automated texture synthesis. In: Proceedings of ICCV, pp. 4501–4510 (2017)
29.
Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of CVPR, pp. 1874–1883 (2016)
30.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of ICLR (2014)
31.
Soviany, P., Ionescu, R.T.: Optimizing the trade-off between single-stage and two-stage deep object detectors using image difficulty prediction. In: Proceedings of SYNASC, pp. 1–6 (2018)
32.
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Proceedings of CVPR, pp. 2440–2448 (2015)
33.
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of CVPR, pp. 1–9, June 2015
34.
Tang, Y., Yan, P., Yuan, Y., Li, X.: Single-image super-resolution via local learning. Int. J. Mach. Learn. Cybern. 2(1), 15–23 (2011) CrossRef
35.
Toderici, G., et al.: Full resolution image compression with recurrent neural networks. In: Proceedings of CVPR, pp. 5306–5314 (2017)
36.
Van Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. In: Proceedings of ICML, pp. 1747–1756 (2016)
37.
Wang, Z., Bovik, A.C.: Mean squared error: Love it or leave it? A new look at signal fidelity measures. Signal Process. Mag. 26(1), 98–117 (2009) CrossRef
38.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004) CrossRef
39.
40.
Yamanaka, J., Kuwashima, S., Kurita, T.: Fast and accurate image super resolution by deep CNN with skip connection and network in network. In: Proceedings of ICONIP, pp. 217–225 (2017) CrossRef
41.
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. In: Proceedings of CVPR, pp. 6721–6729 (2017)
42.
Zhao, G., Liu, J., Jiang, J., Wang, W.: A deep cascade of neural networks for image inpainting, deblurring and denoising. Multimed. Tools Appl. 77(22), 29589–29604 (2018) CrossRef
Metadata
Title
CocoNet: A Deep Neural Network for Mapping Pixel Coordinates to Color Values
Authors
Paul Andrei Bricman
Radu Tudor Ionescu
Copyright Year
2018
Book
Neural Information Processing

Print ISBN: 978-3-030-04178-6

Electronic ISBN: 978-3-030-04179-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-04179-3_6

Premium Partner

Neuer Inhalt
    Image Credits