Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Open accessAbstract
Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters (i.e., linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD-Trainable Nonlinear Reaction Diffusion. The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.
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With variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems.
“Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring).”
From Learning Deep CNN Denoiser Prior for Image Restoration (2017) · cites this paper as [44]
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Shrinkage Fields for Effective Image Restoration
Schmidt, U., & Roth, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.349 · Cited by 543 · see citing works
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983 · see citing works
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Learning Deep CNN Denoiser Prior for Image Restoration
Zhang, K., Zuo, W., Gu, S., & Zhang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.300 · Cited by 2,116 · see citing works
“On the contrary, discriminative learning methods try to learn the prior parameters Θ and a compact inference through an optimization of a loss function on a training set containing degraded-clean image pairs [2, 13, 51, 55, 57].”
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3D Packing for Self-Supervised Monocular Depth Estimation
Guizilini, V., Ambrus, R., Pillai, S., Raventos, A., & Gaidon, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00256 · Cited by 741 · see citing works
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Stochastic Image Denoising by Sampling from the Posterior Distribution
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- Authors
- Chen, Y., & Pock, T.
- Affiliations
- Graz University of Technology (AT)
- AIT Austrian Institute of Technology GmbH (AT)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2017-06-01
- Volume
- 39
- Issue
- 6
- Pages
- 1256-1272
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1906770428
- Cited by
- 1,344
- References
- 4
- Citations by year
- 2026: 49 · 2025: 128 · 2024: 121 · 2023: 139 · 2022: 172 · 2021: 200 · 2020: 210 · 2019: 185 · 2018: 97 · 2017: 38 · 2016: 5
- FWCI
- 73.68
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (FWF)
- China Scholarship Council (CSC) (Y729)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- image restoration, image denoising, single image super-resolution, time-varying parameters, linear filters, influence function, GPU inference
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