Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 1, pp 1553–1584 | Cite as

Super-resolution via adaptive combination of color channels

  • Jian XuEmail author
  • Zhiguo Chang
  • Jiulun Fan
  • Xiaoqiang Zhao
  • Xiaomin Wu
  • Yanzi Wang
  • Xiaodan Zhang
Article
  • 282 Downloads

Abstract

Super-resolution (SR) is a technology to reconstruct a clear high-resolution image with plausible details according to one or a group of observed low- resolution images. However, many existing methods require the help of “tools”, which results in high time and memory costs for training and storing these “tools”. This paper proposes an SR method that can be executed without these training “tools”. This method comprises two stages: color channel adaptive combination and regularization. In the first stage, color channel adaptive combination assembles the textures captured by different color channels into the luminance component. This strategy is helpful in effectively utilizing the luminance information of different light bands. In the second stage, an improved total variation (TV) regularization method is proposed to suppress artifacts and sharpen edges. The TV regularization adds a new item in the iteration formula to enable the edges to be similar to the desired high-resolution image. Next, iterative back projection is used to fit the high-resolution image to the observed low-resolution image. The experimental results demonstrate that the proposed algorithm is superior to many existing learning-based methods and has low time cost.

Keywords

Image up-scaling Super-resolution Total variation regularization Iterative back projection 

Notes

Acknowledgments

This work was supported by the National Science Foundation of China (Grant no. 61340040, 61202183, 61102095, 61201194) and the Science and Technology Plan in Shannxi Province of China (No.2016KJXX-47).

References

  1. 1.
    Bernstein R (1976) Digital image processing of earth observation sensor data. IBM J Res Dev 20(1):40–57CrossRefzbMATHGoogle Scholar
  2. 2.
    Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: IEEE International conference on computer vision and pattern recognition, vol 1. IEEE, Washington, pp 275–282Google Scholar
  3. 3.
    Chen X, Qi C (2014) Low-rank neighbor embedding for single image super-resolution. IEEE Signal Process Lett 21(1):79–82Google Scholar
  4. 4.
    Chen X, Qi C (2014) Nonlinear neighbor embedding for single image super-resolution via kernel mapping. Signal Process 94(1):6–22CrossRefGoogle Scholar
  5. 5.
    Dong W, Shi G, Li X, Zhang L, Wu X (2012) Image reconstruction with locally adaptive sparsity and nonlocal robust regularization. Signal Process Image Commun 27(10):1109–1122CrossRefGoogle Scholar
  6. 6.
    Dong W, Zhang L, Lukac R, Shi G (2013) Sparse representation based image interpolation with nonlocal autoregressive modeling. IEEE Trans Image Process 22(4):1382–1394MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dong W, Zhang L, Shi G, Li X (2013) Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 22(4):1620–1630MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dong W, Zhang L, Shi G, Wu X (2011) Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans Image Process 20(7):1838–1857MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fattal R (2007) Image upsampling via imposed edge statistics. ACM Trans Graphics 26(3):1–8CrossRefGoogle Scholar
  10. 10.
    Feichtenhofer C, Fassold H, Schallauer P (2013) A perceptual image sharpness metric based on local edge gradient analysis. IEEE Signal Process Lett 20(4):379–382CrossRefGoogle Scholar
  11. 11.
    Gao X, Zhang K, Tao D, Li X (2012) Image super-resolution with sparse neighbor embedding. IEEE Trans Image Process 21(7):3194–3205MathSciNetCrossRefGoogle Scholar
  12. 12.
    Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: IEEE Computer society conference on computer vision and pattern recognition. IEEE, Kyoto, pp 349–356Google Scholar
  13. 13.
    Gunturk BK, Altunbasak Y, Mersereau RM (2002) Multiframe resolution-enhancement methods for compressed video. IEEE Signal Process Lett 9 (6):170–174CrossRefGoogle Scholar
  14. 14.
    Hou H, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Transactions on Acoustics. Speech Signal Process 26(6):508–517CrossRefzbMATHGoogle Scholar
  15. 15.
    Irani M, Peleg S (1991) Improving resolution by image registration. CVGIP: Graph Models Image Process 53(3):231–239Google Scholar
  16. 16.
    Jing G, Shi Y, Kong D, Ding W, Yin B (2014) Image super-resolution based on multi-space sparse representation. Multimed Tools Appl 70(2):741–755CrossRefGoogle Scholar
  17. 17.
    Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoustics Speech Signal Process 29(6):1153–1160MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Li M, Nguyen TQ (2008) Markov random field model-based edge-directed image interpolation. IEEE Trans Image Process 17(7):1121–1128MathSciNetCrossRefGoogle Scholar
  19. 19.
    Lysaker M, Osher S, Tai XC (2004) Noise removal using smoothed normals and surface fitting. IEEE Trans Image Process 13(10):1345–1357MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Marquina A, Osher SJ (2008) Image super-resolution by tv-regularization and bregman iteration. J Sci Comput 37(3):367–382MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    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: IEEE International conference on computer vision, vol 2. IEEE, Vancouver, pp 416–423Google Scholar
  22. 22.
    Melin P, Gonzalez C, Castro JR, Mendoza O, Castillo O (2014) Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans Fuzzy Syst 22(6):1515–1525CrossRefGoogle Scholar
  23. 23.
    Nasrollahi K, Moeslund TB (2014) Super-resolution: a comprehensive survey. Mach Vision Appl 25(6):1423–1468CrossRefGoogle Scholar
  24. 24.
    Newton I. (1952) Opticks, or, a treatise of the reflections, refractions, inflections and colours of light. Courier Corporation, CambridgeGoogle Scholar
  25. 25.
    Purkait P, Pal NR, Chanda B (2014) A fuzzy-rule-based approach for single frame super resolution. IEEE Trans Image Process 23(5):2277–2290MathSciNetCrossRefGoogle Scholar
  26. 26.
    Sajjad M, Ejaz N, Mehmood I, Baik S (2013) Digital image super-resolution using adaptive interpolation based on gaussian function. Multimed Tools Appl 9(7):1–17Google Scholar
  27. 27.
    Song H, Huang B, Liu Q, Zhang K (2015) Improving the spatial resolution of landsat tm/etm+ through fusion with spot5 images via learning-based super-resolution. IEEE Trans Geosci Remote Sensing 53(3):1195–1204CrossRefGoogle Scholar
  28. 28.
    Sun J, Xu Z, Shum HY (2011) Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans Image Process 20(6):1529–1542MathSciNetCrossRefGoogle Scholar
  29. 29.
    Timofte R, De Smet V, Van Gool L (2013) Anchored neighborhood regression for fast example-based super-resolution. In: IEEE International conference on computer vision. IEEE, Portland, pp 1920–1927Google Scholar
  30. 30.
    Trinh DH, Luong M, Dibos F, Rocchisani J, Pham C, Nguyen TQ (2014) Novel example-based method for super-resolution and denoising of medical images. IEEE Trans Image Process 23(4):1882–1895MathSciNetCrossRefGoogle Scholar
  31. 31.
    Vedadi F, Shirani S (2014) A map-based image interpolation method via viterbi decoding of markov chains of interpolation functions. IEEE Trans Image Process 23 (1):424–438MathSciNetCrossRefGoogle Scholar
  32. 32.
    Wang J, Zhu S, Gong Y (2010) Resolution enhancement based on learning the sparse association of image patches. Pattern Recog Lett 31(1):1–10CrossRefGoogle Scholar
  33. 33.
    Wang L, Xiang S, Meng G, Wu H y, Pan C (2013) Edge-directed single image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans Circ Syst Video Technol 23(8):1289– 1299CrossRefGoogle Scholar
  34. 34.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612CrossRefGoogle Scholar
  35. 35.
    Xiao Z, Hua H (2012) Super-resolution method for multiview face recognition from a single image per person using nonlinear mappings on coherent features. IEEE Signal Process Lett 19(4):195–198CrossRefGoogle Scholar
  36. 36.
    Yang J, Wang Z, Lin Z, Cohen S, Huang T (2012) Coupled dictionary training for image super-resolution. IEEE Trans Image Process 21(8):3467–3478MathSciNetCrossRefGoogle Scholar
  37. 37.
    Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873MathSciNetCrossRefGoogle Scholar
  38. 38.
    Yang S, Wang M, Chen Y, Sun Y (2012) Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding. IEEE Trans Image Process 21(9):4016–4028MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zeyde R, Protter M, Elad M (2010) On single image scale-up using sparse-representation. Lect Notes Comput Sci 6920(1):711–730MathSciNetzbMATHGoogle Scholar
  40. 40.
    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):4544–4556MathSciNetCrossRefGoogle Scholar
  41. 41.
    Zhang K, Gao X, Tao D, Li X (2013) Single image super-resolution with multiscale similarity learning. IEEE Trans Neural Netw Learn Syst 24(10):1648–1659CrossRefGoogle Scholar
  42. 42.
    Zhou L, Lu X, Yang L (2014) A local structure adaptive super-resolution reconstruction method based on btv regularization. Multimed Tools Appl 71 (3):1879–1892CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Jian Xu
    • 1
    • 2
    • 3
    Email author
  • Zhiguo Chang
    • 4
  • Jiulun Fan
    • 1
  • Xiaoqiang Zhao
    • 1
  • Xiaomin Wu
    • 1
  • Yanzi Wang
    • 1
  • Xiaodan Zhang
    • 1
  1. 1.School of Telecommunication and Information EngineeringXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Image Processing and Recognition CenterXi’an Jiaotong UniversityXi’anChina
  3. 3.Lab of Image ProcessingCrime Scene Investigation Unit of Shannxi ProvinceXi’anChina
  4. 4.School of Information Engineering,Chang’an UniversityXi’anChina

Personalised recommendations