Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution
Received: 29 May 2021 | Revised: 17 July 2021 | Accepted: 21 July 2021 | Online: 21 August 2021
Corresponding author: M. V. Daithankar
Abstract
The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.
Keywords:
observation model, super-resolution, video quality parameters, wavelet residuals, wavelet domain processingDownloads
References
M. V. Daithankar and S. D. Ruikar, "Video Super Resolution: A Review," in ICDSMLA 2019, Singapore, Asia, 2020, pp. 488-495. https://doi.org/10.1007/978-981-15-1420-3_51
M. V. Daithankar and S. D. Ruikar, "Video Super Resolution by Neural Network: A Theoretical Aspect," Journal of Computational and Theoretical Nanoscience, vol. 17, no. 9-10, pp. 4202-4206, Jul. 2020. https://doi.org/10.1166/jctn.2020.9045
G. Pandey and U. Ghanekar, "A compendious study of super-resolution techniques by single image," Optik, vol. 166, pp. 147-160, Aug. 2018. https://doi.org/10.1016/j.ijleo.2018.03.103
L. Yue, H. Shen, J. Li, Q. Yuan, H. Zhang, and L. Zhang, "Image super-resolution: The techniques, applications, and future," Signal Processing, vol. 128, pp. 389-408, Nov. 2016. https://doi.org/10.1016/j.sigpro.2016.05.002
D. Thapa, K. Raahemifar, W. R. Bobier, and V. Lakshminarayanan, "A performance comparison among different super-resolution techniques," Computers & Electrical Engineering, vol. 54, pp. 313-329, Aug. 2016. https://doi.org/10.1016/j.compeleceng.2015.09.011
J. Tian and K.-K. Ma, "A survey on super-resolution imaging," Signal, Image and Video Processing, vol. 5, no. 3, pp. 329-342, Sep. 2011. https://doi.org/10.1007/s11760-010-0204-6
R. Y. Tsai, "Multiframe image restoration and registration," Advances in Computer Vision and Image Processing, vol. 11, no. 2, pp. 317-339, 1984.
S. P. Kim, N. K. Bose, and H. M. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, no. 6, pp. 1013-1027, Jun. 1990. https://doi.org/10.1109/29.56062
S. Rhee and M. G. Kang, "DCT-based regularized algorithm for high-resolution image reconstruction," in International Conference on Image Processing, Kobe, Japan, Oct. 1999, vol. 3, pp. 184-187 vol.3.
X. Zhang and Y. Liu, "A Computationally Efficient Super-Resolution Reconstruction Algorithm Based On The Hybird Interpolation," Journal of Computers, vol. 5, no. 6, pp. 885-892, 2010. https://doi.org/10.4304/jcp.5.6.885-892
X. Li and M. T. Orchard, "New edge-directed interpolation," IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1521-1527, Oct. 2001. https://doi.org/10.1109/83.951537
L. Zhang and X. Wu, "An edge-guided image interpolation algorithm via directional filtering and data fusion," IEEE Transactions on Image Processing, vol. 15, no. 8, pp. 2226-2238, Aug. 2006. https://doi.org/10.1109/TIP.2006.877407
H. Ji and C. Fermuller, "Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 649-660, Apr. 2009. https://doi.org/10.1109/TPAMI.2008.103
A. Muthukrishnan, J. Charles Rajesh Kumar, D. Vinod Kumar, and M. Kanagaraj, "Internet of image things-discrete wavelet transform and Gabor wavelet transform based image enhancement resolution technique for IoT satellite applications," Cognitive Systems Research, vol. 57, pp. 46-53, Oct. 2019. https://doi.org/10.1016/j.cogsys.2018.10.010
S. Izadpanahi and H. Demirel, "Motion based video super resolution using edge directed interpolation and complex wavelet transform," Signal Processing, vol. 93, no. 7, pp. 2076-2086, Jul. 2013. https://doi.org/10.1016/j.sigpro.2013.01.006
W. Witwit, Y. Zhao, K. Jenkins, and S. Addepalli, "Global motion based video super-resolution reconstruction using discrete wavelet transform," Multimedia Tools and Applications, vol. 77, no. 20, pp. 27641-27660, Oct. 2018. https://doi.org/10.1007/s11042-018-5941-5
A. Temizel, "Image Resolution Enhancement using Wavelet Domain Hidden Markov Tree and Coefficient Sign Estimation," in IEEE International Conference on Image Processing, San Antonio, TX, USA, Oct. 2007, vol. 5, pp. V-381-V-384. https://doi.org/10.1109/ICIP.2007.4379845
H. Demirel and G. Anbarjafari, "Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, Jun. 2011. https://doi.org/10.1109/TGRS.2010.2100401
H. Demirel and G. Anbarjafari, "IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition," IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1458-1460, May 2011. https://doi.org/10.1109/TIP.2010.2087767
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004. https://doi.org/10.1109/TIP.2003.819861
Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608-5612, Jun. 2020. https://doi.org/10.48084/etasr.3490
A. Alsheikhy, Y. Said, and M. Barr, "Logo Recognition with the Use of Deep Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6191-6194, Oct. 2020. https://doi.org/10.48084/etasr.3734
"Xiph.org : Derf's Test Media Collection." https://media.xiph.org/video/derf/ (accessed Aug. 02, 2021).
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