Lossy Compression of Noisy Images Using Autoencoders for Computer Vision Applications

[1]  Nick Barnes,et al.  A Deep Journey into Super-resolution , 2019, ACM Comput. Surv..

[2]  Yao Li,et al.  Image Super-Resolution Using VDSR-ResNeXt and SRCGAN , 2018, ArXiv.

[3]  Giuseppe Valenzise,et al.  Quality Assessment of Deep-Learning-Based Image Compression , 2018, 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP).

[4]  Maxime Pelcat,et al.  Study of the Impact of Standard Image Compression Techniques on Performance of Image Classification with a Convolutional Neural Network , 2017 .

[5]  Weisi Lin,et al.  Image Quality Assessment Guided Deep Neural Networks Training , 2017, 1708.03880.

[6]  Rajesh Kumar,et al.  Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving , 2017, 2017 IEEE Region 10 Symposium (TENSYMP).

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jian-Feng Cai,et al.  Data-driven tight frame construction and image denoising , 2014 .

[9]  Kim-Chuan Toh,et al.  An Accelerated Proximal Gradient Algorithm for Frame-Based Image Restoration via the Balanced Approach , 2011, SIAM J. Imaging Sci..

[10]  Raymond H. Chan,et al.  Simultaneously inpainting in image and transformed domains , 2009, Numerische Mathematik.

[11]  Jian-Feng Cai,et al.  A framelet-based image inpainting algorithm , 2008 .

[12]  R. Chan,et al.  Tight frame: an efficient way for high-resolution image reconstruction , 2004 .

[13]  Russell M. Mersereau,et al.  Lossy compression of noisy images , 1998, IEEE Trans. Image Process..

[14]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[15]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.