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Single-Shot Plug-and-Play Methods for Inverse Problems (2311.13682v2)

Published 22 Nov 2023 in cs.CV and eess.IV

Abstract: The utilisation of Plug-and-Play (PnP) priors in inverse problems has become increasingly prominent in recent years. This preference is based on the mathematical equivalence between the general proximal operator and the regularised denoiser, facilitating the adaptation of various off-the-shelf denoiser priors to a wide range of inverse problems. However, existing PnP models predominantly rely on pre-trained denoisers using large datasets. In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data. First, we integrate Single-Shot proximal denoisers into iterative methods, enabling training with single instances. Second, we propose implicit neural priors based on a novel function that preserves relevant frequencies to capture fine details while avoiding the issue of vanishing gradients. We demonstrate, through extensive numerical and visual experiments, that our method leads to better approximations.

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References (49)
  1. Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery. IEEE signal processing magazine, 37(1):105–116, 2020.
  2. Ahmet Oğuz Akyüz et al. Deep joint deinterlacing and denoising for single shot dual-iso hdr reconstruction. IEEE Transactions on Image Processing, 29:7511–7524, 2020.
  3. Densely residual laplacian super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(3):1192–1204, 2020.
  4. Solving inverse problems using data-driven models. Acta Numerica, 28:1–174, 2019.
  5. Törf: Time-of-flight radiance fields for dynamic scene view synthesis. Advances in neural information processing systems, 34:26289–26301, 2021.
  6. Noise2self: Blind denoising by self-supervision. In International Conference on Machine Learning, pages 524–533. PMLR, 2019.
  7. Introduction to inverse problems in imaging. 2021.
  8. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. 2012.
  9. A non-local algorithm for image denoising. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), pages 60–65. Ieee, 2005.
  10. Plug-and-play admm for image restoration: Fixed-point convergence and applications. IEEE Transactions on Computational Imaging, 3(1):84–98, 2016.
  11. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8628–8638, 2021.
  12. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8):2080–2095, 2007.
  13. Anthony J Devaney. Mathematical foundations of imaging, tomography and wavefield inversion. Cambridge University Press, 2012.
  14. Proximal denoiser for convergent plug-and-play optimization with nonconvex regularization. In International Conference on Machine Learning, pages 9483–9505. PMLR, 2022.
  15. Camille Jordan. Sur la series de fourier. CR Acad. Sci., Paris, 92:228–230, 1881.
  16. Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2129–2137, 2019.
  17. ΔΔ\Deltaroman_Δ-prox: Differentiable proximal algorithm modeling for large-scale optimization. ACM Transactions on Graphics (TOG), 42(4):1–19, 2023.
  18. High-quality self-supervised deep image denoising. Advances in Neural Information Processing Systems, 32, 2019.
  19. Deep image prior. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9446–9454. IEEE, 2018.
  20. Convolutional neural networks for inverse problems in imaging: A review. IEEE Signal Processing Magazine, 34(6):85–95, 2017.
  21. Learning proximal operators: Using denoising networks for regularizing inverse imaging problems. In Proceedings of the IEEE International Conference on Computer Vision, pages 1781–1790, 2017.
  22. The monte carlo method. Journal of the American statistical association, 44(247):335–341, 1949.
  23. Shunsuke Ono. Primal-dual plug-and-play image restoration. IEEE Signal Processing Letters, 24(8):1108–1112, 2017.
  24. Self2self with dropout: Learning self-supervised denoising from single image. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1890–1898, 2020.
  25. The little engine that could: Regularization by denoising (red). SIAM Journal on Imaging Sciences, 10(4):1804–1844, 2017.
  26. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015.
  27. Plug-and-play methods provably converge with properly trained denoisers. In International Conference on Machine Learning, pages 5546–5557. PMLR, 2019.
  28. Wire: Wavelet implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18507–18516, 2023.
  29. Implicit neural representations with periodic activation functions. Advances in neural information processing systems, 33:7462–7473, 2020.
  30. Implicit neural representations for image compression. In European Conference on Computer Vision, pages 74–91. Springer, 2022.
  31. An online plug-and-play algorithm for regularized image reconstruction. IEEE Transactions on Computational Imaging, 5(3):395–408, 2019.
  32. Coil: Coordinate-based internal learning for imaging inverse problems. arXiv preprint arXiv:2102.05181, 2021.
  33. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537–7547, 2020.
  34. A convergent image fusion algorithm using scene-adapted gaussian-mixture-based denoising. IEEE Transactions on Image Processing, 28(1):451–463, 2018.
  35. Plug-and-play priors for model based reconstruction. In 2013 IEEE global conference on signal and information processing, pages 945–948. IEEE, 2013.
  36. Curtis R Vogel. Computational methods for inverse problems. SIAM, 2002.
  37. Noise2info: Noisy image to information of noise for self-supervised image denoising. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16034–16043, 2023.
  38. Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 431–441. Springer, 2022.
  39. Tuning-free plug-and-play proximal algorithm for inverse imaging problems. In International Conference on Machine Learning, pages 10158–10169. PMLR, 2020.
  40. Tfpnp: Tuning-free plug-and-play proximal algorithms with applications to inverse imaging problems. The Journal of Machine Learning Research, 23(1):699–746, 2022.
  41. Noise2same: Optimizing a self-supervised bound for image denoising. Advances in neural information processing systems, 33:20320–20330, 2020.
  42. Plug-and-play algorithms for large-scale snapshot compressive imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1447–1457, 2020.
  43. Cycleisp: Real image restoration via improved data synthesis. In IEEE/CVF conference on computer vision and pattern recognition, pages 2696–2705, 2020.
  44. On single image scale-up using sparse-representations. In Curves and Surfaces: 7th International Conference, Avignon, France, June 24-30, 2010, Revised Selected Papers 7, pages 711–730. Springer, 2012.
  45. Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on image processing, 26(7):3142–3155, 2017a.
  46. Learning deep cnn denoiser prior for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3929–3938, 2017b.
  47. Ffdnet: Toward a fast and flexible solution for cnn-based image denoising. IEEE Transactions on Image Processing, 27(9):4608–4622, 2018.
  48. Deep plug-and-play super-resolution for arbitrary blur kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1671–1681, 2019.
  49. Plug-and-play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6360–6376, 2021.
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