Deep Unfolding Network for Image Super-Resolution
Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale f...
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| Vydáno v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 3214 - 3223 |
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01.01.2020
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| ISSN: | 1063-6919 |
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| Abstract | Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such flexibility. To address this issue, this paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods. Specifically, by unfolding the MAP inference via a half-quadratic splitting algorithm, a fixed number of iterations consisting of alternately solving a data subproblem and a prior subproblem can be obtained. The two subproblems then can be solved with neural modules, resulting in an end-to-end trainable, iterative network. As a result, the proposed network inherits the flexibility of 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. Extensive experiments demonstrate the superiority of the proposed deep unfolding network in terms of flexibility, effectiveness and also generalizability. |
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| AbstractList | Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based methods, largely due to the end-to-end training. However, different from model-based methods that can handle the SISR problem with different scale factors, blur kernels and noise levels under a unified MAP (maximum a posteriori) framework, learning-based methods generally lack such flexibility. To address this issue, this paper proposes an end-to-end trainable unfolding network which leverages both learningbased methods and model-based methods. Specifically, by unfolding the MAP inference via a half-quadratic splitting algorithm, a fixed number of iterations consisting of alternately solving a data subproblem and a prior subproblem can be obtained. The two subproblems then can be solved with neural modules, resulting in an end-to-end trainable, iterative network. As a result, the proposed network inherits the flexibility of 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. Extensive experiments demonstrate the superiority of the proposed deep unfolding network in terms of flexibility, effectiveness and also generalizability. |
| Author | Van Gool, Luc Zhang, Kai Timofte, Radu |
| Author_xml | – sequence: 1 givenname: Kai surname: Zhang fullname: Zhang, Kai organization: Computer Vision Lab, ETH Zurich, Switzerland – sequence: 2 givenname: Luc surname: Van Gool fullname: Van Gool, Luc organization: Computer Vision Lab, ETH Zurich, Switzerland – sequence: 3 givenname: Radu surname: Timofte fullname: Timofte, Radu organization: Computer Vision Lab, ETH Zurich, Switzerland |
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| Snippet | Learning-based single image super-resolution (SISR) methods are continuously showing superior effectiveness and efficiency over traditional model-based... |
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| SubjectTerms | Computational modeling Degradation Image resolution Inference algorithms Kernel Learning systems Noise level |
| Title | Deep Unfolding Network for Image Super-Resolution |
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