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Image super resolution based on residual dense CNN and guided filters

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Abstract

Convolutional neural networks (CNNs) have recently made impressive results for image super-resolution (SR). Our goal is to introduce a new image SR framework rely on a CNN. In this paper, the input image is decomposed into luminance channel and chromatic channels. A designed network based on a residual dense network is introduced to extract the hierarchical features from luminance part. The bicubic interpolation is simply used to upscale low resolution (LR) chromatic channels. However, this step degrades the chromatic channels. To tackle this issue, the SR reconstructed luminance channel is applied as the reference image in guided filters to promote the interpolated chromatic channels. Guided filters technique has ability to retain sharp edges and fine details from the reference image and carry them to the target images. Extensive experiments on several commonly used image SR testing datasets demonstrate that our framework has the ability to extract features and outperforms existing well-known techniques for image SR by LR image into the high resolution (HR) image efficiently.

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References

  1. Abbass MY, Kim HW (2018) Blind image separation using pyramid technique. EURASIP Journal on Image and Video Processing 2018 (1)

  2. Abbass MY, Kim H, Abdelwahab SA, Haggag SS, el-Rabaie ESM, Dessouky MI, el-Samie FEA (2019) Image deconvolution using homomorphic technique. SIViP 13:703–709. https://doi.org/10.1007/s11760-018-1399-1

    Article  Google Scholar 

  3. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput & Applic 32:12381–12401. https://doi.org/10.1007/s00521-020-04839-1

    Article  Google Scholar 

  4. Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Comput. https://doi.org/10.1007/s10586-020-03075-5

  5. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047–4071. https://doi.org/10.1007/s10489-018-1190-6

    Article  Google Scholar 

  6. Alam MS, Kwon K-C, Alam MA, Abbass MY, Imtiaz SM, Kim N (2020) Trajectory-based AirWriting recognition using deep neural network and depth sensor. Sensors 20:376

    Article  Google Scholar 

  7. Bevilacqua M, Roumy A, Guillemot C, Alberi Morel M L (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In BMVC

  8. Chen Y, Tan H, Zhang L, Zhou J, Lu Q (2019) Hybrid image super-resolution using perceptual similarity from pre-trained network. J Vis Commun Image R 60:229–235

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: Proceedings of the European Conference on Computer Vision, pp 391–407

  11. Freeman WT, Jones TR, Pasztor EC (2002) Examplebased super-resolution. IEEE Comput Graph Appl 22(2):56–65

    Article  Google Scholar 

  12. Greeshma MS, Bindu VR (2020) Super-resolution Quality Criterion (SRQC): a super-resolution image quality assessment metric. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09352-0

  13. Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars In. IEEE Conference on Computer Vision and Pattern Recognition, pp 5197–5206.

  14. Huang Y, Li J, Gao X, He L, Lu W (2018) Single image super-resolution via multiple mixture prior models. IEEE Trans Image Process 27(12):5904–5917

    Article  MathSciNet  Google Scholar 

  15. Tak Wai Hui, Change Loy Chen, Xiaoou Tang (2016) Depth map super-resolution by deep multi-scale guidance. In: ECCV, pp 353–369.

  16. Khan A, Sohail A, Zahoora U et al (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53:5455–5516. https://doi.org/10.1007/s10462-020-09825-6

  17. Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. TPAMI 32(6):1127–1133

    Article  Google Scholar 

  18. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition. Las Vegas, pp 1646–1654

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

  20. Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep Laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 624–632

  21. Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken AP, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of IEEE conference on computer vision and pattern recognition. CVPR, Honolulu, pp 105–114

    Google Scholar 

  22. Yijun Li, Jia Bin Huang, Narendra Ahuja, Ming Hsuan Yang (2016) Deep joint image filtering. In: ECCV, pp 154–169

  23. Lim B, Son S, Kim H, Nah S, Lee KM (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of IEEE conference on computer vision and pattern recognition. CVPR, Honolulu, pp 1132–1140

    Google Scholar 

  24. Martin C, Fowlkes D, TalJ Malik (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE international conference on, vol 2, pp. 416–423

  25. MohammadAbualigah L, TajudinKhader A, SaidHanandeh E (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artificial Intell 73:111–125. https://doi.org/10.1016/j.engappai.2018.05.003

    Article  Google Scholar 

  26. MohammadAbualigah L, TajudinKhader A, SaidHanandeh E (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Computation Sci 25:456–466. https://doi.org/10.1016/j.jocs.2017.07.018

    Article  Google Scholar 

  27. Nasrollahi H, Farajzadeh K, Hosseini V, Zarezadeh E, Abdollahzadeh M (2020) Deep artifact-free residual network for single-image super-resolution. SIViP 14:407–415. https://doi.org/10.1007/s11760-019-01569-3

    Article  Google Scholar 

  28. Haoyu Ren, Mostafa Elkhamy, Jungwon Lee (2017) Image super resolution based on fusing multiple convolution neural networks. In: CVPR, pp 1050–1057

  29. Shi W, Caballero J, Ledig C, Zhuang X, Bai W, Bhatia K, Antonio M, de Marvao SM, Dawes T, O’Regan D, Rueckert D (2013) Cardiac image super-resolution with global correspondence using multi-atlas patchmatch. In: Proceedings of the International Conference on Medical Image Computing and Computer-as- sisted Intervention. Springer, pp 9–16

  30. Singh A, Ahuja N. (2014) Super-resolution using sub-band self-similarity. In ACCV

  31. Singh A, Singh J (2020) Survey on single image based super-resolution — implementation challenges and solutions. Multimed Tools Appl 79:1641–1672. https://doi.org/10.1007/s11042-019-08254-0

  32. 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

  33. Timofte R, De V,Gool LV (2013) Anchored neighborhood regression for fast example-based super-resolution. In: Proc. IEEE Int Conf Comput Vis, Sydney, pp 1920–1927.

  34. Timofte R, De Smet V, Van Gool L (2014) A+: Adjusted anchored neighborhood regression for fast super-resolution in Proc IEEE Asian Conf Comput Vis, pp 111–126

  35. Timofte R, Agustsson E, Gool LV, Yang M-H, Zhang L, Lim B, Son S, Kim H, Nah S, Lee KM, et al. (2017) Ntire challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp 1110–1121

  36. Tong T, Gen L, Xiejie L, Qinquan G (2017) Image super-resolution using dense skip connections. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4 809–4 817

  37. Tsai RY, Huang TS (1984) Multiframe image restoration and registration. In: Proceedings of Advances in Computer Vision and Image Processing, pp 317–339

  38. Veit A, Wilber MJ, Belongie S (2016) Residual networks behave like ensembles of relatively shallow networks. In: Advances in Neural Information Processing Systems, pp 550–558.

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

  40. Yang J, Wright J, Huang TS, Ma Y (2008) Image super-resolution as sparse representation of raw image patches. In: Proceedings of IEEE Computer Society Confer- ence on Computer Vision and Pattern Recognition. CVPR, Anchorage

    Google Scholar 

  41. Zagoruyko S, Komodakis N (2016) Wide residual networks, arXiv preprint, arXiv: 1605.07146

  42. Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations, In: Proceedings of International Conference on Curves and Surfaces Avignon, pp 711–730.

  43. Zhang Q, Chen B, Lu X, Xia Q (2018) Super-resolution of single multi-color image with guided filter. J Vis Commun Image R 58:277–284. https://doi.org/10.1016/j.jvcir.2018.11.040

    Article  Google Scholar 

  44. Zhang K, Zuo W, Zhang L, (2018) Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3262–3271

  45. Zhang Y, Zhang Y, Zhang J, Xu D, Fu Y, Wang Y, Ji X, Dai Q 2019 Collaborative Representation Cascade for Single-Image Super-Resolution. IEEE Trans Syst Man Cybernetics: Syst 49(5)

  46. Zhao F, Si W, Dou Z (2019) Image super-resolution via two stage coupled dictionary learning. Multimed Tools Appl 78:28453–28460. https://doi.org/10.1007/s11042-017-5493-0

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the Korea Government, under the ITRC (Information Technology Research Center) support program (IITP-2020-2015-0-00448) and Grand Information Technology Research Center support program (IITP-2020-0-01462), supervised by the IITP (Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. School of Information and Communication Engineering, Chungbuk National University, Cheongju, 28644, South Korea

    Mohammed Y. Abbass, Ki-Chul Kwon, Md. Shahinur Alam, Yan-Ling Piao & Nam Kim

  2. Engineering Department, Nuclear Research Center, Atomic Energy Authority, Cairo, Egypt

    Mohammed Y. Abbass

  3. Department of Electronics Engineering, Sunchon National University, 255 Jungang-ro, Suncheon, Jeonnam, 57922, South Korea

    Kwon-Yeon Lee

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  1. Mohammed Y. Abbass
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Correspondence to Nam Kim.

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Abbass, M.Y., Kwon, KC., Alam, M.S. et al. Image super resolution based on residual dense CNN and guided filters. Multimed Tools Appl 80, 5403–5421 (2021). https://doi.org/10.1007/s11042-020-09824-3

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