Abstract
One of the primary measurements of image quality is image resolution. High-resolution images are often required and desired for most of applications as they embody supplementary information. However, the best utilization of image sensors and optical technologies to increase the image pixel density is usually restrictive and overpriced. Therefore, the effective use of image processing techniques for acquiring a high-resolution image generated from low-resolution images is an inexpensive and powerful solution. This kind of image improvement is named image super-resolution. This paper undertakes to investigate the current super-resolution approaches adopted to generate a highresolution image. Furthermore, it highlights the strengths and the limitations of these approaches. More to the point, several image quality metrics are discussed to measure the similarity between the reconstructed image and the original image.
References (60)
- Q. Yuan, L. Zhang, and H. Shen, "Multiframe super-resolution employing a spatially weighted total variation model," IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, pp. 379-392, 2012.
- S. C. Park, M. K. Park, and M. G. Kang, "Super-resolution image reconstruction: a technical overview," IEEE signal processing magazine, vol. 20, pp. 21-36, 2003.
- H. Hou, M. Wang, and X. Wang, "RL-MS-L Filter Function for CT Image Reconstruction," TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 14, pp. 195-202, 2016.
- 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, 2016.
- C. Kumar, "A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising," arXiv preprint arXiv:1102.5688, 2011.
- M. Protter, M. Elad, H. Takeda, and P. Milanfar, "Generalizing the nonlocal-means to super-resolution reconstruction," IEEE Transactions on image processing, vol. 18, pp. 36-51, 2009.
- W. Long, Y. Lu, L. Shen, and Y. Xu, "HIGH-RESOLUTION IMAGE RECONSTRUCTION: AN env 1/TV MODEL AND A FIXED-POINT PROXIMITY ALGORITHM," International Journal of Numerical Analysis & Modeling, vol. 14, 2017.
- R. Tsai and T. S. Huang, "Multiframe image restoration and registration," Advances in computer vision and Image Processing, vol. 1, pp. 317-339, 1984.
- S. Kim, N. K. Bose, and H. Valenzuela, "Recursive reconstruction of high resolution image from noisy undersampled multiframes," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 38, pp. 1013-1027, 1990.
- S. P. Kim and W.-Y. Su, "Recursive high-resolution reconstruction of blurred multiframe images," IEEE Transactions on Image Processing, vol. 2, pp. 534-539, 1993.
- N. Bose, H. Kim, and H. Valenzuela, "Recursive implementation of total least squares algorithm for image reconstruction from noisy, undersampled multiframes," in Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE, Minneapolis MN, vol. V, pp. 269-272, 1993., 1993, pp. 269-272.
- P. Vandewalle, S. Süsstrunk, and M. Vetterli, "A frequency domain approach to registration of aliased images with application to super- resolution," EURASIP Journal on applied signal processing, vol. 2006, pp. 233-233, 2006.
- S. Rhee and M. G. Kang, "Discrete cosine transform based regularized high-resolution image reconstruction algorithm," Optical Engineering, vol. 38, pp. 1348-1356, 1999.
- S. C. Park, M. G. Kang, C. A. Segall, and A. K. Katsaggelos, "Spatially adaptive high-resolution image reconstruction of DCT-based compressed images," IEEE transactions on image processing, vol. 13, pp. 573-585, 2004.
- N. Nguyen and P. Milanfar, "A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution)," Circuits, Systems and Signal Processing, vol. 19, pp. 321-338, 2000.
- S. E. El-Khamy, M. M. Hadhoud, M. I. Dessouky, B. M. Salam, and F. A. El-Samie, "Regularized super-resolution reconstruction of images using wavelet fusion," Optical Engineering, vol. 44, pp. 097001-097001- 10, 2005.
- M. B. Chappalli and N. Bose, "Simultaneous noise filtering and super- resolution with second-generation wavelets," IEEE Signal Processing Letters, vol. 12, pp. 772-775, 2005.
- H. Ji and C. Fermüller, "Wavelet-based super-resolution reconstruction: theory and algorithm," in European Conference on Computer Vision- ECCV 2006, 295-307., 2006, pp. 295-307.
- H. Ji and C. Fermüller, "Robust wavelet-based super-resolution reconstruction: theory and algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, pp. 649-660, 2009.
- X. Li, "Image resolution enhancement via data-driven parametric models in the wavelet space," EURASIP Journal on Image and Video Processing, vol. 2007, pp. 1-12, 2007.
- G. Anbarjafari and H. Demirel, "Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image," ETRI journal, vol. 32, pp. 390-394, 2010.
- N. Nguyen, P. Milanfar, and G. Golub, "A computationally efficient superresolution image reconstruction algorithm," IEEE transactions on image processing, vol. 10, pp. 573-583, 2001.
- X. Li and M. T. Orchard, "New edge-directed interpolation," IEEE transactions on image processing, vol. 10, pp. 1521-1527, 2001.
- L. Zhang and X. Wu, "An edge-guided image interpolation algorithm via directional filtering and data fusion," IEEE transactions on Image Processing, vol. 15, pp. 2226-2238, 2006.
- J. Chu, J. Liu, J. Qiao, X. Wang, and Y. Li, "Gradient-based adaptive interpolation in super-resolution image restoration," in 2008 9th International Conference on Signal Processing, 2008, pp. 1027-1030.
- X. Zhang and Y. Liu, "A Computationally Efficient Super-Resolution Reconstruction Algorithm Based On The Hybird Interpolation," Journal of Computers, vol. 5, pp. 885-892, 2010.
- M. Irani and S. Peleg, "Improving resolution by image registration," CVGIP: Graphical models and image processing, vol. 53, pp. 231-239, 1991.
- H. Stark and P. Oskoui, "High-resolution image recovery from image- plane arrays, using convex projections," JOSA A, vol. 6, pp. 1715-1726, 1989.
- H. Ur and D. Gross, "Improved resolution from subpixel shifted pictures," CVGIP: Graphical Models and Image Processing, vol. 54, pp. 181-186, 1992.
- T. Komatsu, K. Aizawa, T. Igarashi, and T. Saito, "Signal-processing based method for acquiring very high resolution images with multiple cameras and its theoretical analysis," IEE Proceedings I- Communications, Speech and Vision, vol. 140, pp. 19-24, 1993.
- E. Plenge, D. H. Poot, M. Bernsen, G. Kotek, G. Houston, P. Wielopolski, et al., "Super resolution methods in MRI: Can they improve the trade off between resolution, signal to noise ratio, and acquisition time?," Magnetic resonance in medicine, vol. 68, pp. 1983-1993, 2012.
- H. Song, L. Zhang, P. Wang, K. Zhang, and X. Li, "AN adaptive L 1-L 2 hybrid error model to super-resolution," in In Image Processing (ICIP), 2010 17th IEEE International Conference on (pp. 2821-2824). IEEE. , 2010, pp. 2821-2824.
- S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, "Fast and robust multiframe super resolution," IEEE transactions on image processing, vol. 13, pp. 1327-1344, 2004.
- L. Yue, H. Shen, Q. Yuan, and L. Zhang, "A locally adaptive L 1 L 2 norm for multi-frame super-resolution of images with mixed noise and outliers," Signal Processing, vol. 105, pp. 156-174, 2014.
- C. Chen, H. Liang, S. Zhao, Z. Lyu, S. Fang, and X. Pei, "Integrating the Missing Information Estimation into Multi-frame Super-Resolution," Circuits, Systems, and Signal Processing, vol. 35, pp. 1213-1238, 2016.
- X. Zeng and L. Yang, "A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization," Digital Signal Processing, vol. 23, pp. 98-109, 2013.
- S. Mallat and G. Yu, "Super-resolution with sparse mixing estimators," IEEE Transactions on Image Processing, vol. 19, pp. 2889-2900, 2010.
- S. Villena, M. Vega, R. Molina, and A. K. Katsaggelos, "Bayesian super- resolution image reconstruction using an 1 prior," in Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on (pp. 152-157). IEEE. , 2009 , pp. 152-157.
- J. Tian and K.-K. Ma, "Stochastic super-resolution image reconstruction," Journal of Visual Communication and Image Representation, vol. 21, pp. 232-244, 2010.
- S. Villena, M. Vega, S. D. Babacan, R. Molina, and A. K. Katsaggelos, "Bayesian combination of sparse and non-sparse priors in image super resolution," Digital Signal Processing, vol. 23, pp. 530-541, 2013.
- S. P. Belekos, N. P. Galatsanos, and A. K. Katsaggelos, "Maximum a posteriori video super-resolution using a new multichannel image prior," IEEE Transactions on Image Processing, vol. 19, pp. 1451-1464, 2010.
- N. A. Woods, N. P. Galatsanos, and A. K. Katsaggelos, "Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images," IEEE Transactions on Image Processing, vol. 15, pp. 201-213, 2006.
- L. C. Pickup, D. P. Capel, S. J. Roberts, and A. Zisserman, "Bayesian methods for image super-resolution," The Computer Journal, vol. 52, pp. 101-113, 2009.
- G. Polatkan, M. Zhou, L. Carin, D. Blei, and I. Daubechies, "A Bayesian nonparametric approach to image super-resolution," IEEE transactions on pattern analysis and machine intelligence, vol. 37, pp. 346-358, 2015.
- B. C. Tom and A. K. Katsaggelos, "Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low- resolution images," in In Image Processing, 1995. Proceedings., International Conference on (Vol. 2, pp. 539-542). IEEE. , 1995 , pp. 539- 542.
- M. E. Tipping and C. M. Bishop, "Bayesian image super-resolution," In Advances in neural information processing systems (pp. 1303-1310). 2002.
- C. J. Wu, "On the convergence properties of the EM algorithm," The Annals of statistics, pp. 95-103, 1983.
- L. C. Pickup, D. P. Capel, S. J. Roberts, and A. Zisserman, "Bayesian image super-resolution, continued," in NIPS, 2006.
- H. He and L. P. Kondi, "Resolution enhancement of video sequences with simultaneous estimation of the regularization parameter," Journal of Electronic Imaging, vol. 13, pp. 586-596, 2004.
- S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, "An iterative regularization method for total variation-based image restoration," Multiscale Modeling & Simulation, vol. 4, pp. 460-489, 2005.
- R. Pan and S. J. Reeves, "Efficient Huber-Markov edge-preserving image restoration," IEEE Transactions on Image Processing, vol. 15, pp. 3728- 3735, 2006.
- T. Goldstein and S. Osher, "The split Bregman method for L1-regularized problems," SIAM journal on imaging sciences, vol. 2, pp. 323-343, 2009.
- Q. Yuan, L. Zhang, and H. Shen, "Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering," IEEE Transactions on Image Processing, vol. 22, pp. 2327- 2342, 2013.
- Y. Dong, M. Hintermüller, and M. M. Rincon-Camacho, "Automated regularization parameter selection in multi-scale total variation models for image restoration," Journal of Mathematical Imaging and Vision, vol. 40, pp. 82-104, 2011.
- X. Li, Y. Hu, X. Gao, D. Tao, and B. Ning, "A multi-frame image super- resolution method," Signal Processing, vol. 90, pp. 405-414, 2010.
- Z. Tang, M. Deng, C. Xiao, and J. Yu, "Projection onto convex sets super- resolution image reconstruction based on wavelet bi-cubic interpolation," in In Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on (Vol. 1, pp. 351- 354). IEEE., 2011, pp. 351-354.
- I. Begin and F. P. Ferrie, "Comparison of super-resolution algorithms using image quality measures," in Computer and Robot Vision, 2006. The 3rd Canadian Conference on Computer and Robot Vision (CRV'06) 2006, pp. 72-72.
- 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, pp. 600-612, 2004.
- A. Laghrib, A. Ghazdali, A. Hakim, and S. Raghay, "A multi-frame super- resolution using diffusion registration and a nonlocal variational image restoration," Computers & Mathematics with Applications, vol. 72, pp. 2535-2548, 2016.
- R. Nayak and D. Patra, "Super resolution image reconstruction using penalized-spline and phase congruency," Computers & Electrical Engineering, 62, 232-248., 2016.