Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

@article{Bhat2021DeepRO,
  title={Deep Reparametrization of Multi-Frame Super-Resolution and Denoising},
  author={Goutam Bhat and Martin Danelljan and Fisher Yu and Luc Van Gool and Radu Timofte},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={2440-2450}
}
We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction. Our approach thereby leverages… 
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References

SHOWING 1-10 OF 72 REFERENCES
Deep Image Demosaicking using a Cascade of Convolutional Residual Denoising Networks
TLDR
This work proposes a novel deep learning architecture which is inspired by powerful classical image regularization methods and large-scale convex optimization techniques, and shows that the network has the ability to generalize well even when it is trained on small datasets, while keeping the overall number of trainable parameters low.
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
TLDR
This paper proposes the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images and generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications.
Recurrent Back-Projection Network for Video Super-Resolution
We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that
Enhanced Deep Residual Networks for Single Image Super-Resolution
TLDR
This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
Learning a Deep Convolutional Network for Image Super-Resolution
TLDR
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Deep Unfolding Network for Image Super-Resolution
TLDR
This paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning- based methods.
Iterative Residual CNNs for Burst Photography Applications
TLDR
This work focuses on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model, which allows us to restore a single image of higher quality from a sequence of low-quality images as the solution of an optimization problem.
Deep learning for fast super-resolution reconstruction from multiple images
TLDR
This work explores how to exploit CNNs in multiple-image SRR and demonstrates that competitive reconstruction outcome can be obtained within seconds.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
TLDR
HighRes-net is presented, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion, and shows that by learning deep representations of multiple views, it can super-resolve low-resolution signals and enhance Earth Observation data at scale.
Residual Dense Network for Image Super-Resolution
TLDR
This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
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