Single image super resolution via sparse reconstruction

High resolution sensors are required for recognition purposes. Low resolution sensors, however, are still widely used. Software can be used to increase the resolution of such sensors. One way of increasing the resolution of the images produced is using multi-frame super resolution algorithms. Limitation of these methods are that the reconstruction only works if multiple frames are available furthermore these algorithms decreases the temporal resolution. In this paper we use a sparse representation of an overcomplete dictionary to significantly increase the resolution of a single low resolution image. This allows for a higher resolution gain and no loss in temporal resolution. We demonstrate this technique to improve the resolution of number plates images obtained from a near infrared roadside camera.

[1]  Klamer Schutte,et al.  Super-Resolution on Moving Objects and Background , 2006, 2006 International Conference on Image Processing.

[2]  Klamer Schutte,et al.  Multiframe Super-Resolution Reconstruction of Small Moving Objects , 2010, IEEE Transactions on Image Processing.

[3]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[4]  Joseph F. Murray,et al.  Learning Sparse Overcomplete Codes for Images , 2006, J. VLSI Signal Process..

[5]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[6]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[7]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[8]  Michael Elad,et al.  Advances and challenges in super‐resolution , 2004, Int. J. Imaging Syst. Technol..

[9]  Klamer Schutte,et al.  Superresolution reconstruction for moving point target detection , 2008 .

[10]  Klamer Schutte,et al.  Performance Evaluation of Super-Resolution Reconstruction Methods on Real-World Data , 2007, EURASIP J. Adv. Signal Process..