Wavelet Packet Based Video Super-resolution Using Radial Basis Function Neural Network

References

[1] R.Y. Tsai and T.S. Huang, “Multiple-frame image restoration and registration,” Advances in Computer Vision and Image Processing. Greenwich, CT: JAI Press Inc., 1984, pp. 317- 339.

[2] D. Keren, S. Peleg and R. Brada, “Image sequence enhancement using sub -pixel displacements,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 742–746 (June 1988).

[3] M. Irani and S. Peleg, “Improving resolution by image registration” CVGIP: Graphical Models and Image Processing, 53(3): 231–239 (May 1991).

[4] M. Irani and S. Peleg, Motion analysis for image enhancement: Resolution, occlusion and transparency. Journal of Visual Communications and Image Representation, 4: 324–335 (December 1993).

[5] D. Keren, S. Peleg and R. Brada, “Image sequence enhancement using sub-pixel displacements” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 742–746 (June 1988).

[6] H. Stark and P. Oskoui, “High-resolution image recovery from image-plane arrays, using convex projections,” Jour. of Optical Society of America, vol. 6, no.11, pp. 1715- 1726.

[7] Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: “Learning Low-Level Vision,” International Journal of Computer Vision 40(1), 25–47 (2000).

[8] Baker, S., Kanade, T.: “Limits on Super- Resolution and How to Break Them” IEEE Trans. Pattern Anal. and Machine Intell.24(9), 1167– 1183 (2002).

[9] Freeman, W.T., Jones, T.R., Pasztor, E.C.: “Example-Based Super-Resolution” IEEE Computer Graphics and Applications 22(2), 56– 65 (2002).

[10] J. C. Chan, J. Ma and F. Canters, “A comparison of super-resolution reconstruction methods for multi-angle CHRIS/Proba images,” Proc. SPIE Image and Signal Processing for Remote Sensing XIV, vol.7109, no. 1, Oct. 2008.

[11] Brian C. Tom, Aggelos T. Katsaggelos, “Resolution Enhancement of Video Sequences Using Motion Compensation”.

[12] P. Vandewalle, S. S¨usstrunk, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super- resolution,” accepted to EURASIP Journal on Applied Signal Processing, Special Issue on Super-Resolution Imaging, 2005.

[13] Wuqin Tong, Yongshun Ling, Chaochao Huang, Processing method of IR image based on mathematical morphology and wavelet transform[J], Opt. Precision Eng., 15(1), 2007, 138-144.

[14] M. Cocchi, R. Seeber, A. Ulrici et al., WPTER: Wavelet packet transform for efficient pattern recognition of signals [J], Chemo Metrics and Intelligent Laboratory Systems, 57(23), 2001, 97- 119.

[15] Nyquist, H.: Certain topics in “Telegraph Transmission Theory” Trans. Amer. Inst. Elect. Eng. 47, 617–644 (1928).

[16] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 13, no. 4, pp. 600- 612, 2004.

@article{1701529,
      author = {Pann Ei San, Theint Theint Soe, San San Naing},
      title = {Wavelet Packet Based Video Super-resolution Using Radial Basis Function Neural Network},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {2},
      pages = {468-475},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701529.pdf},
      abstract = {Video super-resolution uses a set of successive low-resolution (LR) images in a video sequence to reconstruct a high-quality and high-resolution (HR) image. In this paper, a video super-resolution image reconstruction method using local full search (LFS) motion estimation algorithm, wavelet packet transform (WPT) and radial basis function (RBF) neural network is proposed. LFS motion estimation algorithm is used to collect motion-trace volume which can eliminate deep object motion in the LR images. The HR input image is decomposed by using two-level two-dimension (2D) WPT. The resulted sub-images are employed to train the networks. RBF neural network is applied to each sub-band to learn the relationship between LR and HR images. The super-resolved (SR) image is finally produced by using the inverse WPT. Experiments are mainly focused on image sequences from real videos with different kind of motions. The objective and subjective quality assessments are carried out and compared with the conventional super-resolution methods. The experimental results give that the proposed method outperforms the conventional and recent super-resolution methods for real video sequences. It is well noticed that the proposed method has the best results in terms of both the objective quality and visual quality of the super-resolved image.},
      keywords = {Motion estimation, Radial basis function neural network, Super-resolution, Wavelet packet transform},
      month = {August},
  }