On-demand Learning for Deep Image Restoration

@article{Gao2017OndemandLF,
  title={On-demand Learning for Deep Image Restoration},
  author={Ruohan Gao and Kristen Grauman},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={1095-1104}
}
  • Ruohan Gao, K. Grauman
  • Published 5 December 2016
  • Computer Science
  • 2017 IEEE International Conference on Computer Vision (ICCV)
While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty—such as a certain level of noise or blur. First, we examine the weakness of conventional “fixated” models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration… 

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References

SHOWING 1-10 OF 57 REFERENCES
Natural Image Denoising with Convolutional Networks
TLDR
An approach to low-level vision is presented that combines the use of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise models to avoid computational difficulties in MRF approaches that arise from probabilistic learning and inference.
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep
Image Denoising and Inpainting with Deep Neural Networks
TLDR
A novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA) is presented and can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random.
Deep Convolutional Neural Network for Image Deconvolution
TLDR
This work develops a deep convolutional neural network to capture the characteristics of degradation, establishing the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts.
Semantic Image Inpainting with Perceptual and Contextual Losses
TLDR
A novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network that can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.
Semantic Image Inpainting with Deep Generative Models
TLDR
A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
Context Encoders: Feature Learning by Inpainting
TLDR
It is found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures, and can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.
Shrinkage Fields for Effective Image Restoration
  • Uwe Schmidt, S. Roth
  • Computer Science
    2014 IEEE Conference on Computer Vision and Pattern Recognition
  • 2014
TLDR
This work proposes shrinkage fields, a random field-based architecture that combines the image model and the optimization algorithm in a single unit, and demonstrates state-of-the-art restoration results with high levels of computational efficiency, and significant speedup potential through inherent parallelism.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TLDR
This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
TLDR
This paper proposes to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum, making training deep networks easier and achieving restoration performance gains consequently.
...
...