“Deep bilateral learning for real-time image enhancement”

  • ©Michaël Gharbi, Jiawen Chen, Jonathan T. Barron, Samuel Hasinoff, and Frédo Durand

Conference:


Type:


Title:

    Deep bilateral learning for real-time image enhancement

Session/Category Title: Deep Image Processing


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.

References:


    1. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). http://tensorflow.org/Google Scholar
    2. Andrew Adams, Jongmin Baek, and Myers Abraham Davis. 2010. Fast High-Dimensional Filtering Using the Permutohedral Lattice. Computer Graphics Forum (2010).Google Scholar
    3. Mathieu Aubry, Sylvain Paris, Samuel W Hasinoff, Jan Kautz, and Frédo Durand. 2014. Fast local laplacian filters: Theory and applications. ACM TOG (2014). Google ScholarDigital Library
    4. Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hernández. 2015. Fast bilateral-space stereo for synthetic defocus. CVPR (2015).Google Scholar
    5. Jonathan T Barron and Ben Poole. 2016. The Fast Bilateral Solver. ECCV (2016).Google Scholar
    6. Adrien Bousseau, Sylvain Paris, and Frédo Durand. 2009. User-assisted intrinsic images. ACM TOG (2009). Google ScholarDigital Library
    7. Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. 2011. Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs. CVPR (2011). Google ScholarDigital Library
    8. Jiawen Chen, Andrew Adams, Neal Wadhwa, and Samuel W Hasinoff. 2016. Bilateral guided upsampling. ACM TOG (2016). Google ScholarDigital Library
    9. Jiawen Chen, Sylvain Paris, and Frédo Durand. 2007. Real-time edge-aware image processing with the bilateral grid. ACM TOG (2007). Google ScholarDigital Library
    10. Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. ECCV (2014).Google Scholar
    11. David Eigen, Christian Puhrsch, and Rob Fergus. 2014. Depth map prediction from a single image using a multi-scale deep network. NIPS (2014). Google ScholarDigital Library
    12. Zeev Farbman, Raanan Fattal, and Dani Lischinski. 2011. Convolution pyramids. ACM TOG (2011). Google ScholarDigital Library
    13. Michaël Gharbi, Gaurav Chaurasia, Sylvain Paris, and Frédo Durand. 2016. Deep Joint Demosaicking and Denoising. ACM TOG (2016). Google ScholarDigital Library
    14. Michaël Gharbi, YiChang Shih, Gaurav Chaurasia, Jonathan Ragan-Kelley, Sylvain Paris, and Frédo Durand. 2015. Transform Recipes for Efficient Cloud Photo Enhancement. ACM TOG (2015). Google ScholarDigital Library
    15. Samuel W Hasinoff, Dillon Sharlet, Ryan Geiss, Andrew Adams, Jonathan T Barron, Florian Kainz, Jiawen Chen, and Marc Levoy. 2016. Burst photography for high dynamic range and low-light imaging on mobile cameras. ACM TOG (2016). Google ScholarDigital Library
    16. Kaiming He and Jian Sun. 2015. Fast Guided Filter. CoRR (2015).Google Scholar
    17. Kaiming He, Jian Sun, and Xiaoou Tang. 2013. Guided image filtering. TPAMI (2013).Google Scholar
    18. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. CoRR (2015). Google ScholarDigital Library
    19. James Hegarty, John Brunhaver, Zachary DeVito, Jonathan Ragan-Kelley, Noy Cohen, Steven Bell, Artem Vasilyev, Mark Horowitz, and Pat Hanrahan. 2014. Darkroom: compiling high-level image processing code into hardware pipelines. ACM TOG (2014). Google ScholarDigital Library
    20. Sung Ju Hwang, Ashish Kapoor, and Sing Bing Kang. 2012. Context-based automatic local image enhancement. ECCV (2012).Google Scholar
    21. Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. 2016. Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM TOG (2016). Google ScholarDigital Library
    22. Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. 2016. Flownet 2.0: Evolution of optical flow estimation with deep networks. CoRR (2016).Google Scholar
    23. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ICML (2015). Google ScholarDigital Library
    24. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2016. Image-to-Image Translation with Conditional Adversarial Networks. CoRR (2016).Google Scholar
    25. Max Jaderberg, Karen Simonyan, Andrew Zisserman, and others. 2015. Spatial transformer networks. In Advances in Neural Information Processing Systems. 2017–2025. Google ScholarDigital Library
    26. Vidit Jain and Erik Learned-Miller. 2010. FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010–009. University of Massachusetts, Amherst.Google Scholar
    27. Varun Jampani, Martin Kiefel, and Peter V. Gehler. 2016. Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks. CVPR (2016).Google Scholar
    28. Liad Kaufman, Dani Lischinski, and Michael Werman. 2012. Content-Aware Automatic Photo Enhancement. Computer Graphics Forum (2012). Google ScholarDigital Library
    29. Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. ICLR (2015).Google Scholar
    30. Johannes Kopf, Michael F Cohen, Dani Lischinski, and Matt Uyttendaele. 2007. Joint bilateral upsampling. ACM TOG (2007). Google ScholarDigital Library
    31. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet classification with deep convolutional neural networks. NIPS (2012). Google ScholarDigital Library
    32. Anat Levin, Dani Lischinski, and Yair Weiss. 2008. A closed-form solution to natural image matting. TPAMI (2008). Google ScholarDigital Library
    33. Sifei Liu, Jinshan Pan, and Ming-Hsuan Yang. 2016. Learning recursive filters for low-level vision via a hybrid neural network. ECCV (2016).Google Scholar
    34. Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. CVPR (2015).Google Scholar
    35. Ravi Teja Mullapudi, Andrew Adams, Dillon Sharlet, Jonathan Ragan-Kelley, and Kayvon Fatahalian. 2016. Automatically Scheduling Halide Image Processing Pipelines. ACM TOG (2016). Google ScholarDigital Library
    36. Sylvain Paris and Frédo Durand. 2006. A fast approximation of the bilateral filter using a signal processing approach. ECCV (2006). Google ScholarDigital Library
    37. Sylvain Paris, Samuel W Hasinoff, and Jan Kautz. 2011. Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM TOG (2011). Google ScholarDigital Library
    38. Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris, Marc Levoy, Saman Amarasinghe, and Frédo Durand. 2012. Decoupling Algorithms from Schedules for Easy Optimization of Image Processing Pipelines. ACM TOG (2012). Google ScholarDigital Library
    39. Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Google ScholarCross Ref
    40. Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. 2016. Deep Automatic Portrait Matting. ECCV (2016).Google Scholar
    41. Yichang Shih, Sylvain Paris, Frédo Durand, and William T Freeman. 2013. Data-driven hallucination of different times of day from a single outdoor photo. ACM TOG (2013). Google ScholarDigital Library
    42. Carlo Tomasi and Roberto Manduchi. 1998. Bilateral filtering for gray and color images. ICCV (1998). Google ScholarDigital Library
    43. Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, and Jiaya Jia. 2015. Deep Edge-Aware Filters. ICML (2015). Google ScholarDigital Library
    44. Zhicheng Yan, Hao Zhang, Baoyuan Wang, Sylvain Paris, and Yizhou Yu. 2016. Automatic photo adjustment using deep neural networks. ACM TOG (2016). Google ScholarDigital Library
    45. Fisher Yu and Vladlen Koltun. 2015. Multi-scale context aggregation by dilated convolutions. CoRR (2015).Google Scholar
    46. Lu Yuan and Jian Sun. 2011. High quality image reconstruction from raw and jpeg image pair. ICCV (2011). Google ScholarDigital Library
    47. Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2016. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. CoRR (2016). Google ScholarDigital Library


ACM Digital Library Publication: