Generative Adversarial Nets(GAN)
Papers:
1. I. J. Goodfellow, J.Abadie, M.Mirza, B.Xu, D.W.Farley, S.Ozair, A.Courville, and Y.Bengio, "Generative Adversarial Nets", in NIPS, pp:1-9,2014 [Python] [hacks].
2. Denton, S. Chintala, A. Szlam, and R. Fergus, "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks," arXiv preprint arXiv:1506.05751, 2015 [Python].
3. M.Mirza and S.Osindero,"Conditional generative adversarial nets,"arXiv, pp.1-7, Nov.2014.
4. J.Gauthier, "Conditional generative adversarial nets for convolutional face generation," stanford university, pp.1-9,2014 [Python].
5. D.J. Rezende, S.Mohamed, and D.Wierstra, "Stochastic backpropagation and approximate inference in deep generative models,"Proceedings of Int.Conf. on Machine Learning, May 2014.
Recent Research papers:
7. T.Salimans, I.Goodfellow,W.Zaremba, V.Cheung, A.Radford, and A.Radford, "Improved techniques for training GANs," arXiv June 2016.[code]
8. A.Radford, L.Matz, and S.Chintala,"Unsupervised representation learning with deep convolutional generative adversarial networks,"arXiv 2015.[python] [Tensorflow]
9. D.Jiwoong, C.D.Kim,H.Jiang, and R.Memisevic,"Generating images with recurrent adversarial networks,"arXiv 2016.
10. A.B.L.Larsen, S.K.Sonderby, and O.Winther,"Autoencoding beyond pixels using a learned similarity metric," arxiv 2016.
11. X Chen, Y Duan, R Houthooft, J Schulman,"InfoGAN: Interpretable Representation learning by information maximizing generative adversarial nets,"arXiv 2016 [code].
12. M.Y.Liu and O.Tuzel,"Coupled generative adversarial networks,"nips 2016 [code].
13. Thesis,L., van den oord, A., and Bethge, M."A note on the evaluation of generative models," arxiv nov 2015.
14. K.Lore, A.Akintayo and S.Sarkar, "LLNet: A Deep autoencoder approach to natural low light image enhancement," arXiv April 2016.
Videos:
1. From Facebook AI research - Soumith Chintala - Adversarial Networks, Youtube:01:03:16, 2016/08/10.
2. Generative Adversarial Nets - Fresh Machine Learning #2, Youtube:05:28, 2016/07/10.
3. Emily Denton: Generative image modeling with GAN, Youtube:09:31, 2016/6/27.
4. Aaron Courville: Adversarially learned inference, Youtube:17:02, 2016/06/27.
5. CS231n Winter 2016 Lecture 9 Visualization, Deep Dream, Neural Style, Adversarial Examples, Youtube:01:18:19, 2016/06/25.
6. Deep Advances in Generative Modeling, Youtube:39:30, 2016/03/21.
7.Deep Learning Lecture 14: Karol Gregor on Variational Autoencoders and Image Generation, Youtube:43:17, 2015/03/12.
Self-Study
Books:
1. Neural networks and deep learning, Michael Nielsen, 2015.
Software:
1. Deep learning framework.
Papers:
1. M.D.Zeiler, R.Fergus,"Visualizing and understanding convolutional networks," European conference on computer vision,2014.
2. L. Xu, L.S. Ren, C. Liu, and J. Jia, "Deep convolutional neural network for image deconvolution," Advances in Neural Information Processing Systems, pp. 1790-1798, 2014. [Matlab]
3. Quoc V. Le, "A tutorial on deep learning part 1: Nonlinear classifiers and the backpropagation algorithm", October 2015.
4. Quoc V.Le, "A tutorial on deep learning part II: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks," December 2015.
5. V. Dumoulin and F. Visin, "A guide to convolution arithmetic for deep learning," arxiv March 2016.
Datasets:
1. MNIST for handwritten digits.
2. MIR Flickr dataset.
3. CIFAR
4. LFWcrop face dataset
5. labeled faces in the wild
Books:
1. Neural networks and deep learning, Michael Nielsen, 2015.
Software:
1. Deep learning framework.
Papers:
1. M.D.Zeiler, R.Fergus,"Visualizing and understanding convolutional networks," European conference on computer vision,2014.
2. L. Xu, L.S. Ren, C. Liu, and J. Jia, "Deep convolutional neural network for image deconvolution," Advances in Neural Information Processing Systems, pp. 1790-1798, 2014. [Matlab]
3. Quoc V. Le, "A tutorial on deep learning part 1: Nonlinear classifiers and the backpropagation algorithm", October 2015.
4. Quoc V.Le, "A tutorial on deep learning part II: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks," December 2015.
5. V. Dumoulin and F. Visin, "A guide to convolution arithmetic for deep learning," arxiv March 2016.
Datasets:
1. MNIST for handwritten digits.
2. MIR Flickr dataset.
3. CIFAR
4. LFWcrop face dataset
5. labeled faces in the wild
Deep Neural Networks
- General survey
- M. Egmont-Petersen, D. de Ridder, and H. Handels, "Image processing with neural networks—a review," Pattern recognition 35.10, 2279-2301, 2002.
- Nonlinear filtering
- E. Denton, S. Chintala, A. Szlam, and R. Fergus, "Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks," arXiv preprint arXiv:1506.05751, 2015 [Python].
- L. Xu, L.S. Ren, C. Liu, and J. Jia, "Deep convolutional neural network for image deconvolution," Advances in Neural Information Processing Systems, pp. 1790-1798, 2014. [Matlab]
- DeNoise
- X. Wang, Q. Tao, L. Wang, D. Li, and M. Zhang, “Deep convolutional architecture for natural image denoising,” International Conference on Wireless Communications & Signal Processing, pp. 1–4. 2015.
- F. Agostinelli, M. Anderson, and H. Lee, "Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising," In Advances in Neural Information Processing Systems, pp. 1493-1501, 2013. [Matlab]
- J. Xie, L. Xu, and E. Chen,"Image denoising and inpainting with deep neural networks," Advances in Neural Information Processing Systems, pp. 341–349. 2012. [Matlab]
- H.C. Burger, C.J. Schuler, and S. Harmeling, "Image denoising: Can plain Neural Networks compete with BM3D?" IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392-2399, 2012. [Matlab]
- Image quality assessment
- L. Kang, P. Ye, Y. Li, and D. Doermann, “Convolutional Neural Networks for No-Reference Image Quality Assessment,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740. 2014.
- C. Li, A. Bovik, and X. Wu. "Blind image quality assessment using a general regression neural network." IEEE Transactions on Neural Networks, 22(5):793-799, 2011.
- A. Chetouani, A. Beghdadi, S. Chen, and G. Mostafaoui, "A novel free reference image quality metric using neural network approach," Int. Workshop Video Process. Qual. Metrics Cons. Electron., pages 1-4, Jan. 2010.
- P. Le Callet, C. Viard-Gaudin, and D. Barba, “A Convolutional Neural Network Approach for Objective Video Quality Assessment,” IEEE Trans. Neural Netw., vol. 17, no. 5, pp. 1316–1327, 2006.
- DeHaze
- F. Hussain and J. Jeong, “Visibility Enhancement of Scene Images Degraded by Foggy Weather Conditions with Deep Neural Networks,” Journal of Sensors, vol. 2016, pp. 1–9, 2016.
Noise Modelling
1. A.Foi,M.Trimeche,V.Katkovnik and K.Egiazarian,"Practical Poissonian-Gaussian Noise Modelling and Fitting for Single-Image Raw-Data," IEEE transactions on Image Processing, vol.17, no.10, Oct 2008[Matlab].
2. Alessandro Foi, "Clipped noisy images: Heteroskedastic modeling and practical denoising," Signal Processing, vol.89, no.12,pp.2609-2629, dec 2009.
1. A.Foi,M.Trimeche,V.Katkovnik and K.Egiazarian,"Practical Poissonian-Gaussian Noise Modelling and Fitting for Single-Image Raw-Data," IEEE transactions on Image Processing, vol.17, no.10, Oct 2008[Matlab].
2. Alessandro Foi, "Clipped noisy images: Heteroskedastic modeling and practical denoising," Signal Processing, vol.89, no.12,pp.2609-2629, dec 2009.
Contrast Enhancement
- L. Meylan and S. Susstrunk, “High dynamic range image rendering with a retinex based adaptive filter,” IEEE Trans. on Image Processing, vol. 15, no. 9, pp. 2820-2830, Sep. 2006[Matlab].
- Y. Rao, W. Lin, and L. Chen, “Image-based fusion for video enhancement of night-time surveillance,” Optical Engineering, vol. 49, no. 12, Dec. 2010.
- C. Wang and Z. Ye, “Brightness preserving histogram equalization with maximum entropy: a variational perspective,” IEEE Trans. Consumer Electronics, vol. 51, no. 4, pp. 1326-1334, Nov. 2005.
- Y. Cai, K. Huang, T. Tan, and Y. Wang, “Context enhancement of nighttime surveillance by image fusion,” in Proc. of Int. Conf. Pattern Recognition, Sep. 2006, pp. 980-983.
- A. Yamasaki, H. Takauji, S. Kaneko, T. Kanade, and H. Ohki, “Denighting: enhancement of nighttime image for a surveillance camera,” 19th Int. Conf. on Pattern Recognition, Jan. 2009, pp. 1-4.
- H. Malm, M. Oskarsson, E. Warrant, P. Clarberg, J. Hasselgren, and C. Lejdfors, “Adaptive enhancement and noise reduction in very low light level video,” in Proc. IEEE 11th Int. Conf. Computer Vision, Oct. 2007, pp. 1-8.
- X. Zhang, P. Shen, L. Luo, L. Zhang, and J. Song, “Enhancement and noise reduction of very low light level images,” 21st Int. Conf. on Pattern Recognition, Nov. 2012, pp. 2034-2037.
- A. Rivera, R. Byungyong, and C. Oksam, “Content-aware dark image enhancement through channel division,” IEEE Trans. on Image Processing, vol. 21, no. 9, pp. 3967-3980, May 2012.
- I. S. Jang, T. H. Lee, W. J. Kyung, and Y. Ha, “ Local contrast enhancement based on adaptive multiscale retinex using intensity distribution of input image,” Journal of Imaging Science and Technology, vol. 55, no. 4, pp. 1-14, July 2011.
- E. P Bennett and L. McMillan, “Video enhancement using per-pixel virtual exposures,” in Proc. of ACM SIGGRAPH, vol. 24, July 2005, pp. 845-852.
- S. W. Lee, V. Maik, J. Jang, J. Shin, and J. Paik, “ Noise adaptive spatio-temporal filter for real time noise removal in low light level images,” IEEE Trans. Consumer Electronics, vol. 51, no. 2, pp. 648-653, 2005.
- S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: theory and applications,” Computer Graphics and Vision, vol. 4, no.1, pp. 1-73, 2008.
- M. Mudrova and A. Prochazka, “Principal component analysis in image processing,” Proc. of MATLAB Technical Computing Conference, Prague, 2005.
- Y. K Wang and W. B.Huang, “A CUDA enabled parallel algorithm for accelerating retinex,” Jour. of Real-time Image Processing, vol. 9, no. 3, pp. 407-425, Sep. 2014.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.
- F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high dynamic range images,” ACM Trans. on Graphics, vol. 21, no. 3, pp. 257-266, 2002.
Denoising
(1)General review/survey for denoise
(1)General review/survey for denoise
- P. Milanfar, "A tour of modern image filtering: new insights and methods, both practical and theoretical," IEEE Signal Processing Magazine, 30.1, pp. 106-128, 2013.
- Y. Lou, "Local, Non-local and Global Methods in Image Reconstruction," PhD dissertation, UNIVERSITY OF CALIFORNIA, 2010.
- Nonlocal means(NLM) method:
- (GLIDE) H. Talebi and P. Milanfar, "Global Image Denoising", IEEE Transactions on Image Processing, vol 23, No. 2, pp. 755-768, 2014. [Matlab]
- (NLM) A. Buades, B. Coll, and J.M. Morel, "A review of image denoising algorithms, with a new one," Multiscale Modeling & Simulation 4.2, 490-530, 2005. [Matlab]
- K-SVD:
- R. Rubinstein, M. Zibulevsky, and M. Elad. "Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit," CS Technion 40.8, 1-15, 2008. [Matlab]
- M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, 2006.
- BM3D (Block-matching and 3D filtering):
- K. Dabo, A. Foi, V. Katkovnik, and K. Egiazarian,"Image denoising by sparse 3-d transform-domain collaborative filtering," IEEE Trans. on Image Processing, 16(8), 2080–2095, 2007. [Matlab]
- K. Dabo, A. Foi, V. Katkovnik, and K. Egiazarian," Color Image denoising by sparse 3-d collaborative filtering with grouping constraint in luminance-chrominance space," IEEE Int.Conf. on Image Processing, Oct. 2007,pp.313-316. [Matlab]
- Kernel Regression:
- H. Takeda, S. Farsiu, and P. Milanfar,"Kernel regression for image processing and reconstruction," IEEE Transactions on Image Processing, 16.2, 349-366, 2007. [Matlab]
- GSM (Gaussian Scale Mixtures):
- J. Portilla, V. Strela, M. Wainwright, and E.P. Simoncelli, "Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain," IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1338-1351, 2003. [Matlab]
Image Quality Assessment
- D.M. Chandler, "Seven challenges in image quality assessment: past, present, and future research," ISRN Signal Processing 2013.
- W. Lin and C.C.J. Kuo, "Perceptual visual quality metrics: A survey," Journal of Visual Communication and Image Representation, vol. 22, no. 4, 2011.
- A.K. Moorthy and A.C. Bovik, "Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality'', IEEE Transactions Image Processing, vol. 20, no. 12, pp. 3350-3364, 2011.
- You, J., Reiter, U., Hannuksela, M. M., Gabbouj, M., & Perkis, A. " Perceptual-based quality assessment for audio–visual services: A survey, "Signal Processing: Image Communication, 25(7), 482-501, 2010.
- Z. Wang and A.C. Bovik, "Mean squared error: love it or leave it? A new look at signal fidelity measures," IEEE Signal Processing Magazine, 26.1, 98-117, 2009.