Skip to main content

Advertisement

Springer Nature Link
Log in

Single image super resolution via wavelet transform fusion and SRFeat network

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Image super resolution is a vital research topic in the field of computer vision. It aims to reconstruct high resolution images from low resolution images. Although the conventional image super resolution methods have achieved good performance and effect, there are still have some issues, e.g., the high-frequency details information is insufficient, and the reconstruction process will bring additional noise, and most basic interpolation techniques produce blurry results. To settle the problems mentioned above, we consider combining the deep learning method with the frequency domain fusion method. In this paper, a novel single image super resolution method based on SRFeat network and wavelet fusion is proposed. First, the training image is taken as the input of the backbone SRFeat network, then the generative adversarial network training is carried out. Then, the up-sampling is utilized to obtain the coarse super resolved image. Finally, the output image after the network training is combined with the up-sampling image of the low-resolution image by Wavelet fusion to obtain the final result. Without increasing the depth of the network and the redundant parameters, the proposed method can achieve better reconstruct result. The experimental results show that the proposed method can not only reduce the probability of image distortion, but recover the global information of the reconstructed image and remove the noise brought by the reconstruction process. The PSNR value of the proposed method is improved 0.3 dB, and the SSIM is improved 0.02.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

    Article  Google Scholar 

  • Goodfellow I, Pouget-Abadie J, Mirza M, Xu B et al (2014) Generative adversarial nets. Neural Inf Process Syst 20:2672–2680

    Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. Adaptive computation and machine learning. MIT Press, London, pp 1–775

    MATH  Google Scholar 

  • Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1664–1673

  • Hu C, Zhan S, Wu C (2017) Image super resolution reconstruction based on depth feature learning. J Autom 43(5):814–821

    MATH  Google Scholar 

  • Irani M, Peleg S (1990) Super resolution from image sequences. Int Conf Pattern Recog 2:115–120

    Google Scholar 

  • Kim J, Lee JK, Lee KM (2016a) Deeply-recursive convolutional network for image super resolution. In: IEEE conference on computer vision and pattern recognition, pp 1637–1645

  • Kim J, Lee JK, Lee KM (2016b) Accurate image super resolution using very deep convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 1646–1654

  • Lai WS, Huang JB, Ahuja N et al (2017) Deep laplacian pyramid networks for fast and accurate super resolution. In: IEEE conference on computer vision and pattern recognition, pp 5835–5843

  • Ledig C, Theis L, Huszar F et al (2016) Photo-realistic single image super resolution using a generative adversarial network. In: IEEE conference on computer vision and pattern recognition, pp 105–114

  • Lim B, Son S, Kim H et al (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144

  • Lu H, Wang D, Li Y et al (2019) CONet: a cognitive ocean network. IEEE Wirel Commun 26(3):90–96

    Article  Google Scholar 

  • Nie R, He M, Cao J et al (2019) Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-1098-3

    Article  Google Scholar 

  •  Park SJ, Hyeongseok S, Sunghyun C, Ki-Sang H, Seungyong L (2018) SRFeat: single image super resolution with feature discrimination. Computer vision-ECCV. Springer, Cham, pp 455–471

  • Schultz RR, Stevenson RL (1996) Motion-compensated scan conversion of interlaced video sequences image and video. Int Soc Opt Photon 2666:107–119

    Google Scholar 

  • Shi W, Caballero J, Huszár F et al (2016) Real-time single image and video super-resolution using an efficient subpixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874–1883

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, pp 1–14

  • Stark H, Olsen ET (1992) Projection-based image restoration. J Opt Soc Am 9(11):1914–1919

    Article  Google Scholar 

  • Tai Y, Yang J, Liu X (2017) Image super resolution via deep recursive residual network. IEEE computer vision and pattern recognition. In: IEEE conference on computer vision and pattern recognition, pp 2790–2798

  • Tsai RY, Huang TS (1984) Multiframei mage restoration and registration advances. Comput Vis Image Process 20:317–339

    Google Scholar 

  • Xi C, Shu Z, Dong J et al (2018) Image denoising via deep network based on edge enhancement. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-1036-4

    Article  Google Scholar 

  • Xu X, Lu H, Song J et al (2019) Ternary adversarial networks with self-supervision for zero-shot cross-modal retrieval. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2928180

    Article  Google Scholar 

  • Xu X, Wang T, Yang Y, Zuo L, Shen F, Shen HT (2020) Cross-modal attention with semantic consistence for image-text matching. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/tnnls.2020.2967597

    Article  Google Scholar 

  • Zeng K, Ding S (2017) Advances in image super resolution reconstruction. Comput Eng Appl 53(16):29–35

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  • Zhu S, Zeng B, Liu G et al (2015) Image interpolation based on non-local geometric similarities. In: IEEE international conference on multimedia and expo, pp 1–6

  • Zhou F, Yang W, Liao Q (2012) Interpolation-based image super resolution using multisurface fitting. IEEE Trans Image Process 21(7):3312–3318

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Yangzhou University, Yangzhou, China

    Chunyan Ma, Junwu Zhu, Yujie Li & Yi Jiang

  2. State Key Laboratory of Marine Geology, Tongji University Shanghai, Shanghai, China

    Jianru Li

  3. State Key Laboratoryof Ocean Engineering, Shanghai Jiaotong University, Shanghai, China

    Xin Li

Authors
  1. Chunyan Ma
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Junwu Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yujie Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jianru Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yi Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xin Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Chunyan Ma.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, C., Zhu, J., Li, Y. et al. Single image super resolution via wavelet transform fusion and SRFeat network. J Ambient Intell Human Comput 13, 5023–5031 (2022). https://doi.org/10.1007/s12652-020-02065-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02065-0

Keywords