- Research Article
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Single-Frame Image Super-resolution through Contourlet Learning
EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 073767 (2006)
We propose a learning-based, single-image super-resolution reconstruction technique using the contourlet transform, which is capable of capturing the smoothness along contours making use of directional decompositions. The contourlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images, the inverse contourlet transform of which recovers the super-resolved image. In effect, we learn the high-resolution representation of an oriented edge primitive from the training data. Our experiments show that the proposed approach outperforms standard interpolation techniques as well as a standard (Cartesian) wavelet-based learning both visually and in terms of the PSNR values, especially for images with arbitrarily oriented edges.
Do MN, Vetterli M: The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing 2005, 14(12):2091–2106.
Tsai RY, Huang TS: Multiframe image restoration and registration. In Advances in Computer Vision and Image Processing. Volume 1. JAI Press, Greenwich, Conn, USA; 1984:317–339. chapter 7
Irani M, Peleg S: Improving resolution by image registration. CVGIP: Graphical Models and Image Processing 1991, 53(3):231–239. 10.1016/1049-9652(91)90045-L
Irani M, Peleg S: Motion analysis for image enhancement: resolution, occlusion, and transparency. Journal of Visual Communication and Image Representation 1993, 4(4):324–335. 10.1006/jvci.1993.1030
Tekalp AM, Ozkan MK, Sezan MI: High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), March 1992, San Francisco, Calif, USA 3: 169–172.
Ng MK, Koo J, Bose NK: Constrained total least-squares computations for high-resolution image reconstruction with multisensors. International Journal of Imaging Systems and Technology 2002, 12(1):35–42. 10.1002/ima.10004
Ng MK, Bose NK: Analysis of displacement errors in high-resolution image reconstruction with multisensors. IEEE Transactions on Circuits and SystemsPart I 2002, 49(6):806–813. 10.1109/TCSI.2002.1010035
Ng MK, Bose NK: Fast color image restoration with multisensors. International Journal of Imaging Systems and Technoloy 2002, 12(5):189–197. 10.1002/ima.10028
Nguyen N, Milanfar P, Golub G: A computationally efficient super-resolution image reconstruction algorithm. IEEE Transactions on Image Processing 2001, 10(4):573–583. 10.1109/83.913592
Schultz RR, Stevenson RL: A Bayesian approach to image expansion for improved definition. IEEE Transactions on Image Processing 1994, 3(3):233–242. 10.1109/83.287017
Rajan D, Chaudhuri S: An MRF-based approach to generation of super-resolution images from blurred observations. Journal of Mathematical Imaging and Vision 2002, 16(1):5–15. 10.1023/A:1013961817285
Rajan D, Chaudhuri S: Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(9):1102–1117. 10.1109/TPAMI.2003.1227986
Elad M, Feuer A: Restoration of a single super-resolution image from several blurred, noisy and undersampled measured images. IEEE Transactions on Image Processing 1997, 6(12):1646–1658. 10.1109/83.650118
Lin Z, Shum H-Y: Fundamental limits of reconstruction-based super-resolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(1):83–97. 10.1109/TPAMI.2004.1261081
Baker S, Kanade T: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(9):1167–1183. 10.1109/TPAMI.2002.1033210
Capel D, Zisserman A: Super-resolution from multiple views using learnt image models. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 2: II-627–II-634.
Freeman WT, Jones TR, Pasztor EC: Example-based super-resolution. IEEE Computer Graphics and Applications 2002, 22(2):56–65. 10.1109/38.988747
Hertzmann A, Jacobs CE, Oliver N, Curless B, Salesin DH: Image analogies. Proceedings of ACM SIGGRAPH '01, August 2001, Los Angeles, Calif, USA 327–340.
Joshi MV, Chaudhuri S: A learning-based method for image super-resolution from zoomed observations. Proceedings of 5th International Conference on Advances in Pattern Recognition (ICAPR '03), December 2003, Calcutta, India 179–182.
Jiji CV, Joshi MV, Chaudhuri S: Single-frame image super-resolution using learned wavelet coefficients. International Journal of Imaging Systems and Technology 2004, 14(3):105–112. 10.1002/ima.20013
Gunturk BK, Batur AU, Altunbasak Y, Hayes MH III, Mersereau RM: Eigenface-domain super-resolution for face recognition. IEEE Transactions on Image Processing 2003, 12(5):597–606. 10.1109/TIP.2003.811513
Wang X, Tang X: Hallucinating face by eigentransformation with distortion reduction. Proceedings of 1st International Conference on Biometrics Authentication (ICBA '04), July 2004, Hong Kong 88–94.
Jiji CV, Chaudhuri S: PCA-based generalized interpolation for image super-resolution. Proceedings of 4th Indian Conference on Vision, Graphics & Image Processing (ICVGIP '04), December 2004, Calcutta, India 139–144.
Pickup LC, Roberts SJ, Zisserman A: A sampled texture prior for image super-resolution. In Proceedings of Advances in Neural Information Processing Systems 16 (NIPS '03), 2004, Vancouver, British Columbia, Canada. Edited by: Thrun S, Saul L, Schölkopf B. MIT Press; 1587–1594.
Chang H, Yeung D-Y, Xiong Y: Super-resolution through neighbor embedding. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June–July 2004, Washington, DC, USA 1: I-275–I-282.
Begin I, Ferrie FR: Blind super-resolution using a learning-based approach. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 2: 85–89.
Park J-S, Lee S-W: Enhancing low-resolution facial images using error back-projection for human identification at a distance. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 1: 346–349.
Sun J, Zheng N-N, Tao H, Shum H-Y: Image hallucination with primal sketch priors. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03), June 2003, Madison, Wis, USA 2: II-729–II-736.
Wu J, Trivedi M, Rao B: Resolution enhancement by AdaBoost. Proceedings of 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 893–896.
Do MN: Directional multiresolution image representations, M.S. thesis. Department of Communication, Swiss Federal Institute of Technology, Lausanne, Switzerland; November 2001.
Burt PJ, Adelson EH: The Laplacian pyramid as a compact image code. IEEE Transactions on Communications 1983, 31(4):532–540. 10.1109/TCOM.1983.1095851
Bamberger RH, Smith MJT: A filter bank for the directional decomposition of images: theory and design. IEEE Transactions on Signal Processing 1992, 40(4):882–893. 10.1109/78.127960
Vetterli M: Multidimensional subband coding: some theory and algorithms. Signal Processing 1984, 6(2):97–112. 10.1016/0165-1684(84)90012-4
Park S-I, Smith MJT, Mersereau RM: A new directional filter bank for image analysis and classification. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 3: 1417–1420.
Shapiro JM: Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing 1993, 41(12):3445–3462. 10.1109/78.258085
Vetterli M, Herley C: Wavelets and filter banks: theory and design. IEEE Transactions on Signal Processing 1992, 40(9):2207–2232. 10.1109/78.157221
Phoong S-M, Kim CW, Vaidyanathan PP, Ansari R: A new class of two-channel biorthogonal filter banks and wavelet bases. IEEE Transactions on Signal Processing 1995, 43(3):649–665. 10.1109/78.370620
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Jiji, C., Chaudhuri, S. Single-Frame Image Super-resolution through Contourlet Learning. EURASIP J. Adv. Signal Process. 2006, 073767 (2006). https://doi.org/10.1155/ASP/2006/73767
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