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A Comparative Study on Single Image De-Raining Using Convolutional Neural Network

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Data, Engineering and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 907))

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Abstract

Most of the algorithms for computer vision require a clear image for its processing. For finding the proper solution to the visual degradation of the image due to streaks caused by rain, an effective de-raining algorithm needs to be developed. De-raining is the task of removing streaks of rain and their effect from the distorted image that contains rain. The huge success of methods based on learning and that too CNN-based methods in different domains have prompted its use in de-raining. For effectively obtaining the automatic feature information, deep CNN methods are used. The efficiency of various methods of de-raining is applied and tested on datasets such as Rain1200, Rain100H, Rain100L, Rain1400, and many more that are discussed and analyzed here. The quantitative metrics for the comparison are PSNR and SSIM.

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References

  1. Lan Y, Xia H, Li H, Song S, Wu L (2020) Double recurrent dense network for single image deraining. IEEE Access 8:30615–30627. https://doi.org/10.1109/ACCESS.2020.2972909

    Article  Google Scholar 

  2. Wang C, Wu Y, Cai Y et al (2020) Single image deraining via deep pyramid network with spatial contextual information aggregation. Appl Intell 50:1437–1447. https://doi.org/10.1007/s10489-019-01567-5

  3. Li Y, Tan RT, Guo X, Lu J, Brown MS (2016) Rain streak removal using layer priors. In: CVPR, pp 2736–2744. https://doi.org/10.1109/CVPR.2016.299

  4. Chen Y, Hsu C (2013) A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In: ICCV, pp 1968–1975. https://doi.org/10.1109/ICCV.2013.247

  5. Luo Y, Xu Y, Ji H (2015) Removing rain from a single image via discriminative sparse coding. In: ICCV, pp 3397–3405. https://doi.org/10.1109/ICCV.2015.388

  6. Kim J, Lee C, Sim J, Kim C (2013) Single-image deraining using an adaptive nonlocal means filter. In: ICIP, pp 914–917. https://doi.org/10.1109/ICIP.2013.6738189

  7. Kang L, Lin C, Fu Y (2012) Automatic single-image-based rain streaks removal via image decomposition 21(4):1742–1755. https://doi.org/10.1109/TIP.2011.2179057

  8. Fu X, Huang J, Ding X, Liao Y, Paisley J (2017) Clearing the skies: a deep network architecture for single-image rain removal 26(6):2944–2956. https://doi.org/10.1109/TIP.2017.2691802

    Article  Google Scholar 

  9. Fu X, Huang J, Zeng D, Huang Y, Ding X, Paisley J (2017) Removing rain from single images via a deep detail network. In: CVPR, pp 1715–1723. https://doi.org/10.1109/CVPR.2017.186

  10. Yang W, Tan RT, Feng J, Liu J, Guo Z, Yan S (2017) Deep joint rain detection and removal from a single image. In: CVPR, pp 1685–1694. https://doi.org/10.1109/CVPR.2017.183

  11. Li G, He X, Zhang W, Chang H, Dong L, Lin L (2018) Nonlocally enhanced encoder-decoder network for single image deraining. In: ACM MM, pp 1056–1064. https://doi.org/10.1145/3240508.3240636

  12. Li X, Wu J, Lin Z, Liu H, Zha H (2018) Recurrent squeeze and-excitation context aggregation net for single image deraining. In: ECCV, pp 262–277. https://doi.org/10.1007/978-3-030-01234-216

  13. Wang C et al (2019) Learning a multi-level guided residual network for single image deraining. Signal Process: Image Commun 78:206–215

    Google Scholar 

  14. Xiang P, Wang L, Wu F, Cheng J, Zhou M (2019) Single-image de-raining with feature-supervised generative adversarial network. IEEE Signal Process Lett 26(5):650–654. https://doi.org/10.1109/LSP.2019.2903874

    Article  Google Scholar 

  15. Wang C, Zhang M, Su Z et al (2020) Densely connected multi-scale de-raining net. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08855-0

  16. Peng L, Jiang A, Yi Q, Wang M (2020) Cumulative rain density sensing network for single image derain. IEEE Signal Process Lett 27:406–410. https://doi.org/10.1109/LSP.2020.2974691

    Article  Google Scholar 

  17. Wang C, Fan W, Zhu H et al (2020) Single image deraining via nonlocal squeeze-and-excitation enhancing network. Appl Intell. https://doi.org/10.1007/s10489-020-01693-5

  18. Wang C, Li Z, Wu J, Fan H, Xiao G, Zhang H (2020) Deep Residual Haze network for image dehazing and deraining. IEEE Access 8:9488–9500. https://doi.org/10.1109/ACCESS.2020.2964271

    Article  Google Scholar 

  19. Yang W et al, Scale-free single image deraining via visibility-enhanced recurrent wavelet learning. IEEE Trans Image Process Draft

    Google Scholar 

  20. Jin X, Chen Z, Li W (2020) AI-GAN: asynchronous interactive generative adversarial network for single image rain removal. Pattern Recogn 100:107143

    Google Scholar 

  21. Matsui T, Ikehara M (2020)GAN-based rain noise removal from single-image considering rain composite models. IEEE Access 8:40892–40900. https://doi.org/10.1109/ACCESS.2020.2976761

  22. Sharma PK, Basavaraju S, Sur A (2020) Deep learning-based image de-raining using discrete Fourier transformation. Vis Comput. https://doi.org/10.1007/s00371-020-01971-w

    Article  Google Scholar 

  23. Lin X, Ma L, Sheng B, Wang Z, Chen W,Utilizing two-phase processing with FBLS for single image deraining. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2020.2987703

  24. Wanga X, Chen J, Jianga K, Hana Z, Ruana W, Wanga Z, Lianga C (2020) 417:142–154

    Google Scholar 

  25. Ren W, Tian J, Wang Q, Tang Y (2020) Dually connected deraining net using pixel-wise attention. IEEE Signal Process Lett 27:316–320. https://doi.org/10.1109/LSP.2020.2970345

    Article  Google Scholar 

  26. Fan Y, Chen R, Li Y et al (2020) Deep neural de-raining model based on dynamic fusion of multiple vision tasks. Soft Comput. https://doi.org/10.1007/s00500-020-05291-y

  27. Sharma PK, Basavaraju S, Sur A (2020) High-resolution image de-raining using conditional GAN with sub-pixel upscaling. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09642-7

  28. Li R, Cheong L-F, Tan R (2017) Single image deraining using scale-aware multi-stage recurrent network

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication, UIT, RGPV, Bhopal, India

    Poornima Shrivastava

  2. Department of Information Technology, UIT, RGPV, Bhopal, India

    Roopam Gupta & Asmita A. Moghe

Authors
  1. Poornima Shrivastava
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  2. Roopam Gupta
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  3. Asmita A. Moghe
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Corresponding authors

Correspondence to Poornima Shrivastava or Roopam Gupta .

Editor information

Editors and Affiliations

  1. School of Information Technology, Rajiv Gandhi Technical University, Bhopal, Madhya Pradesh, India

    Sanjeev Sharma

  2. Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan, Taiwan

    Sheng-Lung Peng

  3. Department of Computer Science and Engineering, Rajiv Gandhi Technical University, Bhopal, Madhya Pradesh, India

    Jitendra Agrawal

  4. Department of Computer Science and Engineering, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India

    Rajesh K. Shukla

  5. Department of Information Technology, Haiphong University, Haiphong, Vietnam

    Dac-Nhuong Le

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Shrivastava, P., Gupta, R., Moghe, A.A. (2022). A Comparative Study on Single Image De-Raining Using Convolutional Neural Network. In: Sharma, S., Peng, SL., Agrawal, J., Shukla, R.K., Le, DN. (eds) Data, Engineering and Applications. Lecture Notes in Electrical Engineering, vol 907. Springer, Singapore. https://doi.org/10.1007/978-981-19-4687-5_28

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  • DOI: https://doi.org/10.1007/978-981-19-4687-5_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4686-8

  • Online ISBN: 978-981-19-4687-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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