doi: 10.3934/ipi.2021060
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Generative imaging and image processing via generative encoder

1. 

National University of Singapore, 21 Lower Kent Ridge Rd, Singapore, Singapore 119077

2. 

Purdue University, 610 Purdue Mall, West Lafayette, IN 47907, USA

Received  January 2021 Revised  July 2021 Early access October 2021

This paper introduces a novel generative encoder (GE) framework for generative imaging and image processing tasks like image reconstruction, compression, denoising, inpainting, deblurring, and super-resolution. GE unifies the generative capacity of GANs and the stability of AEs in an optimization framework instead of stacking GANs and AEs into a single network or combining their loss functions as in existing literature. GE provides a novel approach to visualizing relationships between latent spaces and the data space. The GE framework is made up of a pre-training phase and a solving phase. In the former, a GAN with generator
$ G $
capturing the data distribution of a given image set, and an AE network with encoder
$ E $
that compresses images following the estimated distribution by
$ G $
are trained separately, resulting in two latent representations of the data, denoted as the generative and encoding latent space respectively. In the solving phase, given noisy image
$ x = \mathcal{P}(x^*) $
, where
$ x^* $
is the target unknown image,
$ \mathcal{P} $
is an operator adding an addictive, or multiplicative, or convolutional noise, or equivalently given such an image
$ x $
in the compressed domain, i.e., given
$ m = E(x) $
, the two latent spaces are unified via solving the optimization problem
$ z^* = \underset{z}{\mathrm{argmin}} \|E(G(z))-m\|_2^2+\lambda\|z\|_2^2 $
and the image
$ x^* $
is recovered in a generative way via
$ \hat{x}: = G(z^*)\approx x^* $
, where
$ \lambda>0 $
is a hyperparameter. The unification of the two spaces allows improved performance against corresponding GAN and AE networks while visualizing interesting properties in each latent space.
Citation: Yong Zheng Ong, Haizhao Yang. Generative imaging and image processing via generative encoder. Inverse Problems & Imaging, doi: 10.3934/ipi.2021060
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References:
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M. AharonM. Elad and A. Bruckstein, K-svd: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Transactions on Signal Processing, 54 (2006), 4311-4322.   Google Scholar

[2]

M. Arjovsky, S. Chintala and L. Bottou, Wasserstein generative adversarial networks, In Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research, PMLR, International Convention Centre, Sydney, Australia, 70 (2017), 214–223, http://proceedings.mlr.press/v70/arjovsky17a.html. Google Scholar

[3]

D. Bau, J.-Y. Zhu, J. Wulff, W. Peebles, H. Strobelt, B. Zhou and A. Torralba, Seeing what a GAN cannot generate, arXiv: 1910.11626 doi: 10.1109/ICCV.2019.00460.  Google Scholar

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D. Berthelot, T. Schumm and L. Metz, BEGAN: Boundary equilibrium generative adversarial networks, Computer Science, http://arXiv.org/abs/1703.10717. Google Scholar

[5]

A. BoraA. JalalE. Price and A. G. Dimakis, Compressed sensing using generative models, ICML'17 Proceedings of the 34th International Conference on Machine Learning, 70 (2017), 537-546.   Google Scholar

[6]

C. Bowles, L. J. Chen, R. Guerrero, P. Bentley, R. N. Gunn, A. Hammers, D. A. Dickie, M. del C. Valdés Hernández, J. M. Wardlaw and D. Rueckert, Gan augmentation: Augmenting training data using generative adversarial networks, arXiv: 1810.10863. Google Scholar

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A. BuadesB. Coll and J. .Morel, A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2 (2005), 60-65.   Google Scholar

[8]

E. J. CandesJ. Romberg and T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. Inform. Theory, 52 (2006), 489-509.  doi: 10.1109/TIT.2005.862083.  Google Scholar

[9]

J. Chen, J. Chen, H. Chao and M. Yang, Image blind denoising with generative adversarial network based noise modeling, In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. doi: 10.1109/CVPR.2018.00333.  Google Scholar

[10]

T. Chen, X. Zhai, M. Ritter, M. Lucic and N. Houlsby, Self-supervised gans via auxiliary rotation loss, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019), 12146–12155. doi: 10.1109/CVPR.2019.01243.  Google Scholar

[11]

A. Creswell and A. A. Bharath, Inverting the generator of A generative adversarial network, IEEE Transactions on Neural Networks and Learning Systems, 30 (2019), http://arXiv.org/abs/1611.05644. doi: 10.1109/TNNLS.2018.2875194.  Google Scholar

[12]

K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, Bm3d image denoising with shape-adaptive principal component analysis, Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS'09). Google Scholar

[13]

D. Ulyanov, A. Vedaldi and V. Lempitsky, Deep image prior, arXiv: 1711.10925. Google Scholar

[14]

J. Donahue, P. Krähenbühl and T. Darrell, Adversarial feature learning, Computer Science, http://arXiv.org/abs/1605.09782. Google Scholar

[15]

W. DongL. ZhangG. Shi and X. Wu, Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization, IEEE Trans. Image Process., 20 (2011), 1838-1857.  doi: 10.1109/TIP.2011.2108306.  Google Scholar

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[17]

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[18]

P. Getreuer, Total variation inpainting using split bregman, Image Processing On Line, 2 (2012), 147-157.   Google Scholar

[19]

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative adversarial nets, Advances in Neural Information Processing Systems, 27 (NIPS 2014). Google Scholar

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[21]

S.-W. Huang, C.-T. Lin, S.-P. Chen, Y.-Y. Wu, P.-H. Hsu and S.-H. Lai, Auggan: Cross domain adaptation with gan-based data augmentation, In Computer Vision – ECCV 2018, (eds. V. Ferrari, M. Hebert, C. Sminchisescu and Y. Weiss), Springer International Publishing, Cham, (2018), 731–744. Google Scholar

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D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, Published as A Conference Paper at the 3rd International Conference for Learning Representations, San Diego, 2015, 2014, http://arXiv.org/abs/1412.6980. Google Scholar

[24]

D. P. Kingma and M. Welling, Auto-encoding variational bayes, 2013. Google Scholar

[25]

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin and J. Matas, Deblurgan: Blind motion deblurring using conditional adversarial networks, In IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 8183–8192. Google Scholar

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A. B. L. Larsen, S. K. Sønderby and O. Winther, Autoencoding beyond pixels using a learned similarity metric, Computer Science, http://arXiv.org/abs/1512.09300. Google Scholar

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C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang and W. Shi, Photo-realistic single image super-resolution using a generative adversarial network, (2017), 105–114. Google Scholar

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[29]

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Z. C. Lipton and S. Tripathi, Precise recovery of latent vectors from generative adversarial networks, Computer Science, http://arXiv.org/abs/1702.04782. Google Scholar

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Z. Liu, P. Luo, X. Wang and X. Tang, Deep learning face attributes in the wild, In IEEE International Conference on Computer Vision (ICCV), 2015. doi: 10.1109/ICCV.2015.425.  Google Scholar

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S. Menon, A. Damian, S. Hu, N. Ravi and C. Rudin, Pulse: Self-supervised photo upsampling via latent space exploration of generative models, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2002), 2434–2442. Google Scholar

[33]

A. Nguyen, J. Yosinski, Y. Bengio, A. Dosovitskiy and J. Clune, Plug & play generative networks: Conditional iterative generation of images in latent space, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, http://arXiv.org/abs/1612.00005. doi: 10.1109/CVPR.2017.374.  Google Scholar

[34]

D. Pathak, P. Krähenbühl, J. Donahue, T. Darrell and A. Efros, Context encoders: Feature learning by inpainting, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. doi: 10.1109/CVPR.2016.278.  Google Scholar

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A. Radford, L. Metz and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, In ICLR, 2016. Google Scholar

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T. Ramstad, Bentheimer micro-ct with waterflood, 2018, http://www.digitalrocksportal.org/projects/172. Google Scholar

[37]

M. Rosca, B. Lakshminarayanan, D. Warde-Farley and S. Mohamed, Variational approaches for auto-encoding generative adversarial networks, arXiv: 1706.04987. Google Scholar

[38]

L. I. RudinS. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268.  doi: 10.1016/0167-2789(92)90242-F.  Google Scholar

[39]

J. SchlemperJ. CaballeroJ. V. HajnalA. Price and D. Rueckert, A deep cascade of convolutional neural networks for mr image reconstruction, Information Processing in Medical Imaging, 10265 (2017), 647-658.  doi: 10.1007/978-3-319-59050-9_51.  Google Scholar

[40]

V. Shah and C. Hegde, Solving linear inverse problems using gan priors: An algorithm with provable guarantees, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2018), 4609–4613. doi: 10.1109/ICASSP.2018.8462233.  Google Scholar

[41]

Y. Shen, J. Gu, X. Tang and B. Zhou, Interpreting the latent space of gans for semantic face editing, Computer Science, http://arXiv.org/abs/1907.10786. Google Scholar

[42]

D. Ulyanov, A. Vedaldi and V. S. Lempitsky, Adversarial generator-encoder networks, Computer Science, http://arXiv.org/abs/1704.02304. Google Scholar

[43]

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11 (2010), 3371–3408, http://dl.acm.org/citation.cfm?id=1756006.1953039.  Google Scholar

[44]

P. Vincent, H. Larochelle, Y. Bengio and P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, In ICML '08, (2008), 1096–1103. doi: 10.1145/1390156.1390294.  Google Scholar

[45]

Z. WangA. BovikH. Sheikh and E. Simoncelli, Image quality assessment: From error visibility to structural similarity, Image Processing, IEEE Transactions on, 13 (2004), 600-612.  doi: 10.1109/TIP.2003.819861.  Google Scholar

[46]

D. Warde-Farley and Y. Bengio, Improving generative adversarial networks with denoising feature matching, In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings, 2017, https://openreview.net/forum?id=S1X7nhsxl. Google Scholar

[47]

L. Xu and J. Jia, Two-phase kernel estimation for robust motion deblurring, Lecture Notes in Computer Science, 6311 (2010), 157-170.  doi: 10.1007/978-3-642-15549-9_12.  Google Scholar

[48]

Q. Yan and W. Wang, DCGANsfor image super-resolution, denoising and debluring., Google Scholar

[49]

R. Yan and L. Shao, Blind image blur estimation via deep learning, IEEE Trans Image Process, 25 (2016), 1910-1921.   Google Scholar

[50]

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Figure 1.  Flow of training process in GE. Step 1 and 2 forms the pre-training phase, while the remaining form the solving phase
Figure 2.  Reconstruction results on CelebA dataset
Figure 3.  Reconstruction results on Digital Rock dataset
Figure 4.  Reconstruction results on LSUN church dataset
Figure 5.  Denoising results on CelebA dataset
Figure 6.  Deblurring results on CelebA dataset
Figure 7.  Super-resolution results on CelebA dataset
Figure 8.  Inpainting results on CelebA dataset
Figure 9.  Plot of log of average MSE based on number of iterations in the solving phase
Figure 10.  Comparison of image reconstruction of detail region (red box) for original image (left). In order of comparison, from left to right, we have Original, GE, invertGAN, ConvAE
Figure 11.  Comparison of image reconstruction of detail region (red box) for original image (left). In order of comparison, from top to bottom, we have Original, GE, invertGAN, ConvAE
Figure 12.  Additional pore sample result on Digital Rock dataset
Figure 13.  Missing spectacles sample results on CelebA dataset
Figure 14.  Reconstruction results for $ 64\times 64\times 3 $ images in CelebA with GE using BEGAN instead of pGAN
Table 1.  Structure of $ E $. The decoder $ DC $ is a mirror of $ E $ using conv_transpose and upsample
layer type layer
conv2d $ k=[3,3,3,f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,f,2*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,2*f,4*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,4*f,8*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,8*f,16*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,16*f,32*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
fullyconnected $ h=256 $
layer type layer
conv2d $ k=[3,3,3,f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,f,2*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,2*f,4*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,4*f,8*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,8*f,16*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
conv2d $ k=[3,3,16*f,32*f],s=[1,1],a=ReLU $
maxpool2d $ k=[1,2,2,1],s=[2,2] $
fullyconnected $ h=256 $
Table 2.  Quantitative results comparing models for CelebA. Additionally, some FID scores reported by recent GAN papers that used CelebA $ 128\times128\times3 $ images are also presented for comparison, labelled with *
Model MSE SSIM FID
CRGAN* 16.97
SSGAN* 24.36
Our pGAN 22.13
ConvAE 0.03386 0.6823$ \pm $0.051 87.71
AEGAN 0.03317 0.6907$ \pm $0.050 34.53
invertGAN 0.03529 0.7203$ \pm $0.038 19.19
GE 0.03262 0.7329$ \pm $0.025 17.42
Model MSE SSIM FID
CRGAN* 16.97
SSGAN* 24.36
Our pGAN 22.13
ConvAE 0.03386 0.6823$ \pm $0.051 87.71
AEGAN 0.03317 0.6907$ \pm $0.050 34.53
invertGAN 0.03529 0.7203$ \pm $0.038 19.19
GE 0.03262 0.7329$ \pm $0.025 17.42
Table 3.  Quantitative results comparing models for digital rocks. The number in brackets show the size of the latent vector in pGAN that the model is trained on. Models with same latent sizes are solved with the same pGAN weights. The same AE is used for all models
Model MSE PSNR
ConvAE 0.009271 20.32
invertGAN (512) 0.008185 20.86
GE (512) 0.007470 21.26
GE (256) 0.007741 21.11
GE (128) 0.007839 21.05
GE (64) 0.008499 20.70
Model MSE PSNR
ConvAE 0.009271 20.32
invertGAN (512) 0.008185 20.86
GE (512) 0.007470 21.26
GE (256) 0.007741 21.11
GE (128) 0.007839 21.05
GE (64) 0.008499 20.70
Table 4.  Results of invertGAN, GE on spectacles. T refers to samples which produced spectacles, F refers to samples which did not. Remaining are invalid reconstructions
InvertGAN, F InvertGAN, T
GE, F 289 32
GE, T 157 469
InvertGAN, F InvertGAN, T
GE, F 289 32
GE, T 157 469
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