Multi-Frame Super-Resolution Survey

Mahmoud Moustafa Khattab

Abstract


The image resolution is probably the primary measurements of the image quality. The image with higher resolution is required and generally desired in the majority of applications, because of it represents the additional information inside the image. However, the best using of image sensors and optical technologies is normally a high priced and also restrictive method to increase the image pixel density. Therefore, the effective use of image processing techniques for acquiring a high-resolution image generated from low-resolution images is an inexpensive and a powerful solution. This kind of image improvement is termed super-resolution image. Numerous strategies like frequency domain, interpolation, and regularization techniques are proposed for generating the super resolution image. In this paper, a general survey of the available multi-frame super resolution approaches is explained. Finally, several image quality metrics are discussed to measure the similarity between the reconstructed image and the original image. 


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Anbarjafari, G., & Demirel, H. (2010). Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image. ETRI journal, 32(3), 390-394.

Begin, I., & Ferrie, F. P. (2006). Comparison of super-resolution algorithms using image quality measures. Paper presented at the Computer and Robot Vision, 2006. The 3rd Canadian Conference on.

Belekos, S. P., Galatsanos, N. P., & Katsaggelos, A. K. (2010). Maximum a posteriori video super-resolution using a new multichannel image prior. IEEE Transactions on Image Processing, 19(6), 1451-1464.

Bose, N., Kim, H., & Valenzuela, H. (1993). Recursive implementation of total least squares algorithm for image reconstruction from noisy, undersampled multiframes. Paper presented at the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on.

Bose, N. K., & Ahuja, N. A. (2006). Superresolution and noise filtering using moving least squares. IEEE Transactions on Image Processing, 15(8), 2239-2248.

Chappalli, M. B., & Bose, N. (2005). Simultaneous noise filtering and super-resolution with second-generation wavelets. IEEE Signal Processing Letters, 12(11), 772-775.

Chu, J., Liu, J., Qiao, J., Wang, X., & Li, Y. (2008). Gradient-based adaptive interpolation in super-resolution image restoration. Paper presented at the 2008 9th International Conference on Signal Processing.

Dong, Y., Hintermüller, M., & Rincon-Camacho, M. M. (2011). Automated regularization parameter selection in multi-scale total variation models for image restoration. Journal of Mathematical Imaging and Vision, 40(1), 82-104.

El-Khamy, S. E., Hadhoud, M. M., Dessouky, M. I., Salam, B. M., & El-Samie, F. A. (2005). Regularized super-resolution reconstruction of images using wavelet fusion. Optical Engineering, 44(9), 097001-097001-097010.

Farsiu, S., Robinson, M. D., Elad, M., & Milanfar, P. (2004). Fast and robust multiframe super resolution. IEEE Transactions on Image Processing, 13(10), 1327-1344.

Goldstein, T., & Osher, S. (2009). The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), 323-343.

He, H., & Kondi, L. P. (2004). Resolution enhancement of video sequences with simultaneous estimation of the regularization parameter. Journal of Electronic Imaging, 13(3), 586-596.

Irani, M., & Peleg, S. (1991). Improving resolution by image registration. CVGIP: Graphical Models and Image Processing, 53(3), 231-239.

Ji, H., & Fermüller, C. (2006). Wavelet-based super-resolution reconstruction: theory and algorithm. Paper presented at the European Conference on Computer Vision.

Ji, H., & Fermüller, C. (2009). Robust wavelet-based super-resolution reconstruction: theory and algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4), 649-660.

Kim, S., Bose, N. K., & Valenzuela, H. (1990). Recursive reconstruction of high resolution image from noisy undersampled multiframes. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(6), 1013-1027.

Kim, S. P., & Su, W.-Y. (1993). Recursive high-resolution reconstruction of blurred multiframe images. IEEE Transactions on Image Processing, 2(4), 534-539.

Komatsu, T., Aizawa, K., Igarashi, T., & Saito, T. (1993). Signal-processing based method for acquiring very high resolution images with multiple cameras and its theoretical analysis. IEE Proceedings I-Communications, Speech and Vision, 140(1), 19-24.

Kumar, C. (2011). A novel super resolution reconstruction of low reoslution images progressively using dct and zonal filter based denoising. arXiv preprint arXiv:1102.5688.

Li, X. (2007). Image resolution enhancement via data-driven parametric models in the wavelet space. EURASIP Journal on Image and Video Processing, 2007(1), 1-12.

Li, X., Hu, Y., Gao, X., Tao, D., & Ning, B. (2010). A multi-frame image super-resolution method. Signal Processing, 90(2), 405-414.

Li, X., & Orchard, M. T. (2001). New edge-directed interpolation. IEEE Transactions on Image Processing, 10(10), 1521-1527.

Mallat, S., & Yu, G. (2010). Super-resolution with sparse mixing estimators. IEEE Transactions on Image Processing, 19(11), 2889-2900.

Nguyen, N., & Milanfar, P. (2000). A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution). Circuits, Systems and Signal Processing, 19(4), 321-338.

Nguyen, N., Milanfar, P., & Golub, G. (2001). A computationally efficient superresolution image reconstruction algorithm. IEEE Transactions on Image Processing, 10(4), 573-583.

Osher, S., Burger, M., Goldfarb, D., Xu, J., & Yin, W. (2005). An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation, 4(2), 460-489.

Pan, R., & Reeves, S. J. (2006). Efficient Huber-Markov edge-preserving image restoration. IEEE Transactions on Image Processing, 15(12), 3728-3735.

Panda, S. S., & Jena, G. (2016). Image Super Resolution Using Wavelet Transformation Based Genetic Algorithm Computational Intelligence in Data Mining—Volume 2 (pp. 355-361): Springer.

Park, S. C., Kang, M. G., Segall, C. A., & Katsaggelos, A. K. (2004). Spatially adaptive high-resolution image reconstruction of DCT-based compressed images. IEEE Transactions on Image Processing, 13(4), 573-585.

Park, S. C., Park, M. K., & Kang, M. G. (2003). Super-resolution image reconstruction: a technical overview. IEEE signal processing magazine, 20(3), 21-36.

Pickup, L. C., Capel, D. P., Roberts, S. J., & Zisserman, A. (2006). Bayesian image super-resolution, continued. Paper presented at the NIPS.

Pickup, L. C., Capel, D. P., Roberts, S. J., & Zisserman, A. (2009). Bayesian methods for image super-resolution. The Computer Journal, 52(1), 101-113.

Plenge, E., Poot, D. H., Bernsen, M., Kotek, G., Houston, G., Wielopolski, P., . . . Meijering, E. (2012). Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time? Magnetic resonance in medicine, 68(6), 1983-1993.

Polatkan, G., Zhou, M., Carin, L., Blei, D., & Daubechies, I. (2015). A Bayesian nonparametric approach to image super-resolution. IEEE transactions on pattern analysis and machine intelligence, 37(2), 346-358.

Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51.

Rhee, S., & Kang, M. G. (1999). Discrete cosine transform based regularized high-resolution image reconstruction algorithm. Optical Engineering, 38(8), 1348-1356.

Shen, H., Peng, L., Yue, L., Yuan, Q., & Zhang, L. (2016). Adaptive norm selection for regularized image restoration and super-resolution. IEEE transactions on cybernetics, 46(6), 1388-1399.

Song, H., Zhang, L., Wang, P., Zhang, K., & Li, X. (2010). AN adaptive L 1–L 2 hybrid error model to super-resolution. Paper presented at the Image Processing (ICIP), 2010 17th IEEE International Conference on.

Stark, H., & Oskoui, P. (1989). High-resolution image recovery from image-plane arrays, using convex projections. JOSA A, 6(11), 1715-1726.

Tang, Z., Deng, M., Xiao, C., & Yu, J. (2011). Projection onto convex sets super-resolution image reconstruction based on wavelet bi-cubic interpolation. Paper presented at the Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on.

Tian, J., & Ma, K.-K. (2010). Stochastic super-resolution image reconstruction. Journal of Visual Communication and Image Representation, 21(3), 232-244.

Tipping, M. E., & Bishop, C. M. (2002). Bayesian image super-resolution. Paper presented at the NIPS.

Tom, B. C., & Katsaggelos, A. K. (1995). Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images. Paper presented at the Image Processing, 1995. Proceedings., International Conference on.

Tsai, R., & Huang, T. S. (1984). Multiframe image restoration and registration. Advances in computer vision and Image Processing, 1(2), 317-339.

Ur, H., & Gross, D. (1992). Improved resolution from subpixel shifted pictures. CVGIP: Graphical Models and Image Processing, 54(2), 181-186.

Vandewalle, P., Süsstrunk, S., & Vetterli, M. (2006). A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP Journal on applied signal processing, 2006, 233-233.

Villena, S., Vega, M., Babacan, S. D., Molina, R., & Katsaggelos, A. K. (2013). Bayesian combination of sparse and non-sparse priors in image super resolution. Digital Signal Processing, 23(2), 530-541.

Villena, S., Vega, M., Molina, R., & Katsaggelos, A. K. (2009). Bayesian super-resolution image reconstruction using an ℓ1 prior. Paper presented at the Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on.

Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612.

Woods, N. A., Galatsanos, N. P., & Katsaggelos, A. K. (2006). Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images. IEEE Transactions on Image Processing, 15(1), 201-213.

Wu, C. J. (1983). On the convergence properties of the EM algorithm. The Annals of statistics, 95-103.

Yuan, Q., Zhang, L., & Shen, H. (2012). Multiframe super-resolution employing a spatially weighted total variation model. IEEE Transactions on Circuits and Systems for Video Technology, 22(3), 379-392.

Yuan, Q., Zhang, L., & Shen, H. (2013). Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering. IEEE Transactions on Image Processing, 22(6), 2327-2342.

Yue, L., Shen, H., Yuan, Q., & Zhang, L. (2014). A locally adaptive L 1− L 2 norm for multi-frame super-resolution of images with mixed noise and outliers. Signal Processing, 105, 156-174.

Zadeh, P. B., & Akbari, A. S. (2012). Image resolution enhancement using multi-wavelet and cycle-spinning. Paper presented at the Control (CONTROL), 2012 UKACC International Conference on.

Zeng, X., & Yang, L. (2013). A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization. Digital Signal Processing, 23(1), 98-109.

Zhang, L., & Wu, X. (2006). An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Transactions on Image Processing, 15(8), 2226-2238.

Zhang, X., & Liu, Y. (2010). A Computationally Efficient Super-Resolution Reconstruction Algorithm Based On The Hybird Interpolation. Journal of Computers, 5(6), 885-892.


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