- Research Article
- Open Access
Adaptive Outlier Rejection in Image Super-resolution
EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 038052 (2006)
One critical aspect to achieve efficient implementations of image super-resolution is the need for accurate subpixel registration of the input images. The overall performance of super-resolution algorithms is particularly degraded in the presence of persistent outliers, for which registration has failed. To enhance the robustness of processing against this problem, we propose in this paper an integrated adaptive filtering method to reject the outlier image regions. In the process of combining the gradient images due to each low-resolution image, we use adaptive FIR filtering. The coefficients of the FIR filter are updated using the LMS algorithm, which automatically isolates the outlier image regions by decreasing the corresponding coefficients. The adaptation criterion of the LMS estimator is the error between the median of the samples from the LR images and the output of the FIR filter. Through simulated experiments on synthetic images and on real camera images, we show that the proposed technique performs well in the presence of motion outliers. This relatively simple and fast mechanism enables to add robustness in practical implementations of image super-resolution, while still being effective against Gaussian noise in the image formation model.
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
Chaudhuri S (Ed): Super-Resolution Imaging. Kluwer Academic, Boston, Mass, USA; 2001.
Park SC, Park MK, Kang MG: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 2003, 20(3):21–36. 10.1109/MSP.2003.1203207
Baker S, Kanade T: Super-resolution optical flow. In Tech. Rep. CMU-RI-TR-99-36. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pa, USA; 1999.
Schultz RR, Stevenson RL: Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 1996, 5(6):996–1011. 10.1109/83.503915
Hardie RC, Barnard KJ, Armstrong EE: Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE Transactions on Image Processing 1997, 6(12):1621–1633. 10.1109/83.650116
Lee ES, Kang MG: Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration. IEEE Transactions on Image Processing 2003, 12(7):826–837. 10.1109/TIP.2003.811488
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
Farsiu S, Robinson D, Elad M, Milanfar P: Robust shift and add approach to super-resolution. Applications of Digital Image Processing XXVI, August 2003, San Diego, Calif, USA, Proceedings of SPIE 5203: 121–130.
Farsiu S, Robinson MD, Elad M, Milanfar P: Fast and robust multiframe super-resolution. IEEE Transactions on Image Processing 2004, 13(10):1327–1344. 10.1109/TIP.2004.834669
Zomet A, Rav-Acha A, Peleg S: Robust super-resolution. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 645–650.
Astola J, Kuosmanen P: Fundamentals of Nonlinear Digital Filtering. CRC Press, New York, NY, USA; 1997.
Trimeche M, Yrjänäinen J: Order filters in super-resolution image reconstruction. Image Processing: Algorithms and Systems II, January 2003, Santa Clara, Calif, USA, Proceedings of SPIE 5014: 190–200.
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: 627–634.
Elad M, Feuer A: Super-resolution reconstruction of image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 1999, 21(9):817–834. 10.1109/34.790425
Bertero M, Boccacci P: Introduction to Inverse Problems in Imaging. Institute of Physics Publishing (IOP), Bristol, UK; 1998. chapter 6
Haykin S: Adaptive Filter Theory. 3rd edition. Prentice-Hall, Englewood Cliffs, NJ, USA; 1996.
Max N: Visualizing Hilbert curves. Proceedings of IEEE Visualization '98, October 1998, Research Triangle Park, NC, USA 447–450, 564.
Perez A, Kamata S, Kawaguchi E: Peano scanning of arbitrary size images. Proceedings of 11th IAPR-IEEE International Conference on Pattern Recognition (ICPR '92), August–September 1992, The Hague, the Netherlands 3: 565–568.
About this article
Cite this article
Trimeche, M., Bilcu, R.C. & Yrjänäinen, J. Adaptive Outlier Rejection in Image Super-resolution. EURASIP J. Adv. Signal Process. 2006, 038052 (2006). https://doi.org/10.1155/ASP/2006/38052
- Synthetic Image
- Adaptation Criterion
- Fast Mechanism
- Outlier Rejection
- Real Camera