Skip to main content
Log in

Memory-Augmented Deep Unfolding Network for Guided Image Super-resolution

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. However, previous model-based methods mainly take the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image, simply ignoring many non-local common characteristics between them. To alleviate this issue, we firstly propose a maximum a posteriori (MAP) estimation model for GISR with two types of priors on the HR target image, i.e., local implicit prior and global implicit prior. The local implicit prior aims to model the complex relationship between the HR target image and the HR guidance image from a local perspective, and the global implicit prior considers the non-local auto-regression property between the two images from a global perspective. Secondly, we design a novel alternating optimization algorithm to solve this model for GISR. The algorithm is in a concise framework that facilitates to be replicated into commonly used deep network structures. Thirdly, to reduce the information loss across iterative stages, the persistent memory mechanism is introduced to augment the information representation by exploiting the Long short-term memory unit (LSTM) in the image and feature spaces. In this way, a deep network with certain interpretation and high representation ability is built. Extensive experimental results validate the superiority of our method on a variety of GISR tasks, including Pan-sharpening, depth image super-resolution, and MR image super-resolution. Code will be released at https://github.com/manman1995/pansharpening.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  • Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., & Bruce, L. M. (2007). Comparison of pansharpening algorithms: Outcome of the 2006 grs-s data fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3012–3021.

    Article  Google Scholar 

  • Bahrampour, S., Nasrabadi, N. M., Ray, A., & Jenkins, W. K. (2015). Multimodal task-driven dictionary learning for image classification. IEEE Transactions on Image Processing, 25(1), 24–38.

    Article  MathSciNet  MATH  Google Scholar 

  • Bruna, J., Sprechmann, P., & LeCun, Y. (2015). Super-resolution with deep convolutional sufficient statistics. arXiv preprint arXiv:1511.05666

  • Cai, J., & Huang, B. (2020). Super-resolution-guided progressive Pansharpening based on a deep convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59(6), 5206–20.

    Article  Google Scholar 

  • Cao, X., Fu, X., Hong, D., Xu, Z., & Meng, D. (2021). Pancsc-net: A model-driven deep unfolding method for pansharpening. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3115501

    Article  Google Scholar 

  • Dai, S., Han, M., Xu, W., Wu, Y., & Gong, Y. (2007). Soft edge smoothness prior for alpha channel super resolution. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1–8

  • Deng, X., & Dragotti, P. L. (2019). Deep coupled ISTA network for multi-modal image super-resolution. IEEE Transactions on Image Processing, 29, 1683–1698.

    Article  MathSciNet  MATH  Google Scholar 

  • Deng, X., & Dragotti, P. L. (2020). Deep convolutional neural network for multi-modal image restoration and fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(10), 3333–48.

    Article  Google Scholar 

  • Diebel, J., & Thrun, S. (2005). An application of markov random fields to range sensing. In: NIPS

  • Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295–307.

    Article  Google Scholar 

  • Dong, C., Loy, C.C., & Tang, X. (2016). Accelerating the super-resolution convolutional neural network. In: European conference on computer vision, Springer, pp 391–407

  • Dong, W., Zhang, L., Shi, G., & Li, X. (2012). Nonlocally centralized sparse representation for image restoration. IEEE Transactions on Image Processing, 22(4), 1620–1630.

    Article  MathSciNet  MATH  Google Scholar 

  • Dong, W., Zhang, L., Lukac, R., & Shi, G. (2013). Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Transactions on Image Processing, 22(4), 1382–1394. https://doi.org/10.1109/TIP.2012.2231086

    Article  MathSciNet  MATH  Google Scholar 

  • Feng, C.M., Fu, H., Yuan, S., & Xu, Y. (2021). Multi-contrast mri super-resolution via a multi-stage integration network. arXiv preprint arXiv:2105.08949

  • Ferstl, D., Reinbacher, C., Ranftl, R., Ruether, M., & Bischof, H. (2013). Image guided depth upsampling using anisotropic total generalized variation. In: 2013 IEEE International Conference on Computer Vision, pp 993–1000, https://doi.org/10.1109/ICCV.2013.127

  • Geman, D., & Reynolds, G. (1992). Constrained restoration and the recovery of discontinuities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(3), 367–383.

    Article  Google Scholar 

  • Geman, D., & Yang, C. (1995). Nonlinear image recovery with half-quadratic regularization. IEEE Transactions on Image Processing, 4(7), 932–946.

    Article  Google Scholar 

  • Gillespie, A. R., Kahle, A. B., & Walker, R. E. (1987). Color enhancement of highly correlated images. ii. channel ratio and “chromaticity’’ transformation techniques - sciencedirect. Remote Sensing of Environment, 22(3), 343–365.

    Article  Google Scholar 

  • Gregor, K., & LeCun, Y. (2010). Learning fast approximations of sparse coding. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, pp 399–406

  • Guo, C., Li, C., Guo, J., Cong, R., Fu, H., & Han, P. (2018). Hierarchical features driven residual learning for depth map super-resolution. IEEE Transactions on Image Processing, 28(5), 2545–2557.

    Article  MathSciNet  Google Scholar 

  • Ham, B., Cho, M., & Ponce, J. (2015). Robust image filtering using joint static and dynamic guidance. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4823–4831, https://doi.org/10.1109/CVPR.2015.7299115

  • Ham, B., Cho, M., & Ponce, J. (2017). Robust guided image filtering using nonconvex potentials. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1), 192–207.

    Article  Google Scholar 

  • Haydn, R., Dalke, G. W., Henkel, J., & Bare, J. E. (1982). Application of the IHS color transform to the processing of multisensor data and image enhancement. National Academy of Sciences of the United States of America, 79(13), 571–577.

    Google Scholar 

  • He, K., Sun, J., & Tang, X. (2012). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409.

    Article  Google Scholar 

  • He, K., Sun, J., & Tang, X. (2013). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397–1409.

    Article  Google Scholar 

  • He, R., Zheng, W. S., Tan, T., & Sun, Z. (2014). Half-quadratic-based iterative minimization for robust sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(2), 261–275. https://doi.org/10.1109/TPAMI.2013.102

    Article  Google Scholar 

  • Hirschmuller, H., & Scharstein, D. (2007). Evaluation of cost functions for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–8

  • Hui, T.W., Loy, C.C., & Tang, X. (2016). Depth map super-resolution by deep multi-scale guidance. In: European Conference on Computer Vision, Springer, pp 353–369

  • J.R.H. Yuhas, A.F.G., & Boardman, J.M. (1992). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm. Proc Summaries Annu JPL Airborne Geosci Workshop pp 147–149

  • Jia, K., Wang, X., & Tang, X. (2012). Image transformation based on learning dictionaries across image spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2), 367–380.

    Article  Google Scholar 

  • Jing, X.Y., Zhu, X., Wu, F., You, X., Liu, Q., Yue, D., Hu, R., & Xu, B. (2015). Super-resolution person re-identification with semi-coupled low-rank discriminant dictionary learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 695–704

  • Kim, B., Ponce, J., & Ham, B. (2021). Deformable kernel networks for joint image filtering. International Journal of Computer Vision, 129(2), 579–600.

    Article  Google Scholar 

  • Kim, J., Lee, J.K., & Lee, K.M. (2016a). Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1646–1654

  • Kim, J., Lee, J.K., & Lee, K.M. (2016b). Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1637–1645

  • Kingma, D.P., & Ba, J. (2017). Adam: A method for stochastic optimization. arXiv:1412.6980

  • Kopf, J., Cohen, M., Lischinski, D., & Uyttendaele, M. (2007a). Joint bilateral upsampling. In: ACM Transactions on Graphics (Proceedings of SIGGRAPH 2007), vol 26

  • Kopf, J., Cohen, M. F., Lischinski, D., & Uyttendaele, M. (2007). Joint bilateral upsampling. ACM Transactions on Graphics (ToG), 26(3), 96.

    Article  Google Scholar 

  • Krishnan, D., & Fergus, R. (2009). Fast image deconvolution using hyper-laplacian priors. Advances in Neural Information Processing Systems, 22, 1033–1041.

    Google Scholar 

  • Laben, C.A., & Brower, B.V. (2000). Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. US Patent 6,011,875

  • Li, Y., Huang, J., Ahuja, N., & Yang, M. (2016a). Deep joint image filtering. In: Computer Vision - 14th European Conference, ECCV 2016, Proceedings, Germany, pp 154–169, https://doi.org/10.1007/978-3-319-46493-0_10

  • Li, Y., Huang, J.B., Ahuja, N., & Yang, M.H. (2016b). Deep joint image filtering. In: European Conference on Computer Vision, Springer, pp 154–169

  • Li, Y., Huang, J. B., Ahuja, N., & Yang, M. H. (2019). Joint image filtering with deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1909–1923. https://doi.org/10.1109/TPAMI.2018.2890623

    Article  Google Scholar 

  • Liao, W., Xin, H., Coillie, F.V., Thoonen, G., & Philips, W. (2017). Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter. In: Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

  • Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., & Huang, T. S. (2016). Robust single image super-resolution via deep networks with sparse prior. IEEE Transactions on Image Processing, 25(7), 3194–3207.

    Article  MathSciNet  MATH  Google Scholar 

  • Liu, J. G. (2000). Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461–3472.

    Article  Google Scholar 

  • Liu, X., Song, M., Tao, D., Zhou, X., Chen, C., & Bu, J. (2014). Semi-supervised coupled dictionary learning for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3550–3557

  • Lu, S., Ren, X., & Liu, F. (2014). Depth enhancement via low-rank matrix completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3390–3397

  • Lyu, Q., Shan, H., Steber, C., Helis, C., Whitlow, C., Chan, M., & Wang, G. (2020). Multi-contrast super-resolution MRI through a progressive network. IEEE Transactions on Medical Imaging, 39(9), 2738–2749.

    Article  Google Scholar 

  • Mallat, S., & Yu, G. (2010). Super-resolution with sparse mixing estimators. IEEE Transactions on Image Processing, 19(11), 2889–2900.

    Article  MathSciNet  MATH  Google Scholar 

  • Mao, X., Shen, C., & Yang, Y. B. (2016). Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in Neural Information Processing Systems, 29, 2802–2810.

    Google Scholar 

  • Marivani, I., Tsiligianni, E., Cornelis, B., & Deligiannis, N. (2020). Multimodal deep unfolding for guided image super-resolution. IEEE Transactions on Image Processing, 29, 8443–8456.

    Article  MATH  Google Scholar 

  • Masi, G., Cozzolino, D., Verdoliva, L., & Scarpa, G. (2016). Pansharpening by convolutional neural networks. Remote Sensing, 8(7), 594.

    Article  Google Scholar 

  • Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A.Y. (2011). Multimodal deep learning. In: IEEE International Conference on Machine Learning (ICML)

  • Oktay, O., Bai, W., Lee, M., Guerrero, R., Kamnitsas, K., Caballero, J., de Marvao, A., Cook, S., O’Regan, D., & Rueckert, D. (2016). Multi-input cardiac image super-resolution using convolutional neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, pp 246–254

  • Park, J., Kim, H., Tai, Y.W., Brown, M.S., & Kweon, I. (2011). High quality depth map upsampling for 3d-tof cameras. In: 2011 International Conference on Computer Vision, pp 1623–1630, https://doi.org/10.1109/ICCV.2011.6126423

  • Paszke, A., Gross, S., Massa, F., Lerer, A., & Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library

  • Pham, C.H., Ducournau, A., Fablet, R., & Rousseau, F. (2017). Brain mri super-resolution using deep 3d convolutional networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE, pp 197–200

  • Rockafellar, R. T. (1976). Monotone operators and the proximal point algorithm. Siam J Control Optim, 14(5), 877–898.

    Article  MathSciNet  MATH  Google Scholar 

  • Sanchez-Beato, A., & Pajares, G. (2008). Noniterative interpolation-based super-resolution minimizing aliasing in the reconstructed image. IEEE Transactions on Image Processing, 17(10), 1817–1826.

    Article  MathSciNet  MATH  Google Scholar 

  • Scharstein, D., & Pal, C. (2007). Learning conditional random fields for stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1–8

  • Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1), 7–42.

    Article  MATH  Google Scholar 

  • Shen, X., Yan, Q., Xu, L., Ma, L., & Jia, J. (2015). Multispectral joint image restoration via optimizing a scale map. IEEE transactions on pattern analysis and machine intelligence, 37(12), 2518–2530.

    Article  Google Scholar 

  • Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012). Indoor segmentation and support inference from rgb-d images. In: Proceedings of the European Conference on Computer Vision, pp 746–760

  • Song, J., Chen, B., & Zhang, J. (2021). Memory-augmented deep unfolding network for compressive sensing. In: ACM MM

  • Song, P., Deng, X., Mota, J. F., Deligiannis, N., Dragotti, P. L., & Rodrigues, M. R. (2019). Multimodal image super-resolution via joint sparse representations induced by coupled dictionaries. IEEE Transactions on Computational Imaging, 6, 57–72.

    Article  MathSciNet  Google Scholar 

  • Su, H., Jampani, V., Sun, D., Gallo, O., Learned-Miller, E., & Kautz, J. (2019). Pixel-adaptive convolutional neural networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 11158–11167, https://doi.org/10.1109/CVPR.2019.01142

  • Sun, B., Ye, X., Li, B., Li, H., Wang, Z., & Xu, R. (2021). Learning scene structure guidance via cross-task knowledge transfer for single depth super-resolution. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 7788–7797, https://doi.org/10.1109/CVPR46437.2021.00770

  • Sun, J., Xu, Z., & Shum, H.Y. (2008). Image super-resolution using gradient profile prior. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1–8

  • Tai, Y., Yang, J., & Liu, X. (2017). Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3147–3155

  • Timofte, R., De Smet, V., & Van Gool, L. (2013). Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1920–1927

  • Timofte, R., De Smet, V., & Van Gool, L. (2014). A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision, Springer, pp 111–126

  • Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. In: Proceedings of the IEEE International Conference on Computer Vision, IEEE, pp 839–846

  • Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A., Restaino, R., & Wald, L. (2014). A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565–2586.

    Article  Google Scholar 

  • Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63, 691–699.

    Google Scholar 

  • Wang, J., Chen, Y., Wu, Y., Shi, J., & Gee, J. (2020). Enhanced generative adversarial network for 3d brain mri super-resolution. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 3627–3636

  • Wang, S., Zhang, L., Liang, Y., & Pan, Q. (2012). Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 2216–2223

  • Wang, X., Girshick, R., Gupta, A., & He, K. (2018). Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7794–7803

  • Wu, H., Zheng, S., Zhang, J., & Huang, K. (2018a). Fast end-to-end trainable guided filter. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1838–1847

  • Wu, H., Zheng, S., Zhang, J., & Huang, K. (2018b). Fast end-to-end trainable guided filter. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 1838–1847, https://doi.org/10.1109/CVPR.2018.00197

  • Xu, S., Zhang, J., Zhao, Z., Sun, K., Liu, J., & Zhang, C. (2021). Deep gradient projection networks for pan-sharpening. In: CVPR, pp 1366–1375

  • Yang, J., Wright, J., Huang, T., & Ma, Y. (2008). Image super-resolution as sparse representation of raw image patches. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1–8

  • Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 19(11), 2861–2873.

    Article  MathSciNet  MATH  Google Scholar 

  • Yang, J., Wang, Z., Lin, Z., Cohen, S., & Huang, T. (2012). Coupled dictionary training for image super-resolution. IEEE Transactions on Image Processing, 21(8), 3467–3478.

    Article  MathSciNet  MATH  Google Scholar 

  • Yang, J., Lin, Z., & Cohen, S. (2013). Fast image super-resolution based on in-place example regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1059–1066

  • Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., & Paisley, J. (2017). Pannet: A deep network architecture for pan-sharpening. In: IEEE International Conference on Computer Vision, pp 5449–5457

  • Ye, X., Sun, B., Wang, Z., Yang, J., Xu, R., Li, H., & Li, B. (2020). Pmbanet: Progressive multi-branch aggregation network for scene depth super-resolution. IEEE Transactions on Image Processing, 29, 7427–7442. https://doi.org/10.1109/TIP.2020.3002664

    Article  MATH  Google Scholar 

  • Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018). A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 978–989.

    Article  Google Scholar 

  • Zeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z. (2018). Simultaneous single-and multi-contrast super-resolution for brain mri images based on a convolutional neural network. Computers in Biology and Medicine, 99, 133–141.

    Article  Google Scholar 

  • Zhang, K., Gool, L.V., & Timofte, R. (2020). Deep unfolding network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 3217–3226

  • Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. (2018a). Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 286–301

  • Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018b). Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2472–2481

  • Zhou, M., Fu, X., Huang, J., Zhao, F., Liu, A., & Wang, R. (2022). Effective pan-sharpening with transformer and invertible neural network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–15. https://doi.org/10.1109/TGRS.2021.3137967

    Article  Google Scholar 

  • Zhou, M., Huang, J., Fang, Y., Fu, X., & Liu, A. (2022b). Pan-sharpening with customized transformer and invertible neural network. In: Thirty-Six AAAI Conference on Artificial Intelligence

  • Zhou, M., Yan, K., Huang, J., Yang, Z., Fu, X., & Zhao, F. (2022c). Mutual information-driven pan-sharpening. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1798–1808

  • Zhuang, Y.T., Wang, Y.F., Wu, F., Zhang, Y., & Lu, W.M. (2013). Supervised coupled dictionary learning with group structures for multi-modal retrieval. In: Twenty-Seventh AAAI Conference on Artificial Intelligence

Download references

Acknowledgements

This work was supported by National Key Research and Development Project of China (2021ZD0110700), National Natural Science Foundation of China (62272375, 61906151, 62050194, 62037001), Innovative Research Group of the National Natural Science Foundation of China(61721002), Innovation Research Team of Ministry of Education (IRT\(\_\)17R86), Project of China Knowledge Centre for Engineering Science and Technology, and Project of XJTU Undergraduate Teaching Reform (20JX04Y).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiangyong Cao.

Additional information

Communicated by Yu Li.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, M., Yan, K., Pan, J. et al. Memory-Augmented Deep Unfolding Network for Guided Image Super-resolution. Int J Comput Vis 131, 215–242 (2023). https://doi.org/10.1007/s11263-022-01699-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-022-01699-1

Keywords

Navigation