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Image super-resolution with densely connected convolutional networks

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Abstract

This paper proposes a new model for single image super-resolution (SR) task by utilizing the design of densely connected convolutional networks (DenseNet). The proposed method is an end-to-end model which is able to learn mapping between low- and high-resolution images. The proposed method takes the low-resolution images as input and generates its high-resolution version. Unlike those conventional methods which adjust each component of convolutional networks separately, our model jointly optimizes all layers. Besides, the proposed model has a lightweight structure and is extensively evaluated on widely adopted data sets. In our experiments, the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively. In addition, we also carried out experiments in terms of different designs and configurations to achieve better balance between reconstruction performance and speed in this paper.

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References

  1. Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: Proceedings of IEEE international conference on computer vision. Kyoto, pp 349–356

  2. Dai D, Timofte R, Van Gool L (2015) Jointly optimized regressors for image super-resolution. Comput Graph Forum 34(2):95–104

    Article  Google Scholar 

  3. Freedman G, Fattal R (2011) Image and video upscaling from local selfexamples. ACM Trans Graphs (TOG) 30(2):12

    Google Scholar 

  4. Chang H, Yeung DY, Xiong Y (2004) Super resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer society conference on IEEE computer vision and pattern recognition, vol 1, no 1. Washington, DC, pp 1–1

  5. Cui Z, Chang H, Shan S, Zhong B, Chen X (2014) Deep network cascade for image super-resolution. European conference on computer vision. Springer, Cham, pp 49–64

    Google Scholar 

  6. Bevilacqua M, Roumy A, Guillemot C, Morel MLA (2012) Low-complexity single-image super- resolution based on nonnegative neighbor embedding. In: Proc Brit Mach vis conf, pp 1–10

  7. Yang C-Y, Yang M-H (2013) Fast direct super-resolution by simple functions. In: Proceedings of IEEE International conference on computer vision. Sydney, pp 561–568

  8. Freeman WT, Pasztor EC, Carmichael OT (2000) Learning lowlevel vision. Int J Comput Vis 40 (1):25–47

    Article  MATH  Google Scholar 

  9. Freeman WT, Jones TR, Pasztor EC (2002) Example-based superresolution. IEEE Comput Graph Appl 22(2):56–65

    Article  Google Scholar 

  10. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  11. Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: International conference on curves and surfaces. Springer, Berlin, pp 711–730

  12. Schulter S, Lesistner C, Bischof H (2015) Fast and accutate image upscaling with super-resulution forests. In: Proceedings of IEEE Conference on computer vision and pattern recognition, pp 184–199

  13. Yang CY, Ma C, Yang MH (2014) Single-image super-resolution: Abenchmark. In: European Conference on computer vision. Springer, Cham, pp 372–386

  14. Freeman WT, Pasztor EC, Carmichael OT (2000) Learning low-level vision. Int J Comput Vis 40 (11):25–47

    Article  MATH  Google Scholar 

  15. Huang JJ, Siu WC, Liu TR (2015) Fast image interpolation via random forests. IEEE Trans Image Process 24(10):3232–3245

    Article  MathSciNet  Google Scholar 

  16. Salvador J, PerezPellitero E (2015) Naive Bayes super-resulution forest. In: Proceedings of IEEE International conference on computer vision, pp 325–333

  17. Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proc IEEE Conf Comput vis pattern recognit, pp 5197–5206

  18. Yang J, Lin Z, Cohen S (2013) Fast image super-resolution based on in-place example regression. In: 2013 IEEE Conference on IEEE computer vision and pattern recognition (CVPR), pp 1059–1066

  19. 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, Cham, pp 111–126

  20. Jia K, Wang X, Tang X (2013) Image transformation based on learning dictionaries across image spaces. IEEE Trans Pattern Anal Mach Intell 35(11):367–380

    Article  Google Scholar 

  21. Yang J, Wang Z, Lin Z, Cohen S, Huang T (2012) Coupled dictionary training for image super resolution. IEEE Trans Image Process 21(8):3467–3478

    Article  MathSciNet  MATH  Google Scholar 

  22. Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127–1133

    Article  Google Scholar 

  23. Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 3791–3799

  24. Timofte R, Smet V, Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of IEEE International conference on computer vision, pp 1920–1927

  25. Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Proceedings of European conference on computer vision, pp 184–199

  26. Dong C, Loy CC, He K (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307

    Article  Google Scholar 

  27. Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  28. Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. In: Proc. 7th Int Conf curves surfaces, pp 711–730

  29. Yang J, Wright J, Huang T, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: Proc IEEE Conf comput vis pattern recog, pp 1–8

  30. Shi W, Caballero J, Huszar F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video superresolution using an efficient sub-pixel convolutional neural network. In: Proceedings of IEEE Conference on computer vision and pattern recognition, pp 1874–1883

  31. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of IEEE Conference on computer vision and pattern recognition. arXiv:http://arXiv.org/abs/1511.04587

  32. Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160

    Article  MathSciNet  MATH  Google Scholar 

  33. Wang Z, Liu D, Yang J, Han W, Huang T (2015) Deep networks for image super-resolution with sparse prior. In: Proceedings of IEEE International conference on computer vision, pp 370–378

  34. Duchon CE (1979) Lanczos filtering in one and two dimensions. J Appl Meteorol 18(8):1016–1022

    Article  Google Scholar 

  35. Sun J, Sun J, Xu Z, Shum H-Y (2008) Image super-resolution using gradient profile prior. In: Proceedings of IEEE Conference on computer vision and pattern recognition, pp 1–8

  36. Protter M, Elad M, Takeda H, Milanfar P (2009) Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans Image Process 18(1):36–51

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with non-local means and steering kernel regression. IEEE Trans Image Process 21(11):4544–4556

    Article  MathSciNet  MATH  Google Scholar 

  38. Baker S, Kanade T (2000) Limits on super-resolution and how to break them[J]. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183

    Article  Google Scholar 

  39. Gu S, Zuo W, Xie Q, Meng D, Feng X, Zhang L (2015) Convolutional sparse coding for image super-resolution. In: Proceedings of IEEE International conference on computer vision, pp 1823–1831

  40. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of IEEE International conference on computer vision, vol 2, pp 416–423

  41. Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel M-L (2012) Lowcomplexity single-image super-resolution based on nonnegative neighbor embedding, 1–10

  42. He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proc IEEE Conf comput vis pattern recog, pp 379–3799

  43. Wang D, Li M (2017) Deep stochastic configuration networks with universal approximation property. arXiv:http://arXiv.org/abs/1702.05639

  44. Jaderberg M, Dalibard V, Osindero S, et al. (2017) Population based training of neural networks. arXiv:http://arXiv.org/abs/1711.09846

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Correspondence to Tingsong Ma.

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Kuang, P., Ma, T., Chen, Z. et al. Image super-resolution with densely connected convolutional networks. Appl Intell 49, 125–136 (2019). https://doi.org/10.1007/s10489-018-1234-y

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  • DOI: https://doi.org/10.1007/s10489-018-1234-y

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