--- abstract: 'Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.' altloc: [] chapter: ~ commentary: ~ commref: ~ confdates: 'September 11-15, 2005' conference: International Conference on Artificial Neural Networks confloc: 'Warsaw, Poland' contact_email: ~ creators_id: [] creators_name: - family: Miravet given: Carlos honourific: '' lineage: '' - family: Rodriguez given: Francisco B. honourific: '' lineage: '' date: 2005 date_type: published datestamp: 2005-10-20 department: ~ dir: disk0/00/00/45/67 edit_lock_since: ~ edit_lock_until: ~ edit_lock_user: ~ editors_id: [] editors_name: [] eprint_status: archive eprintid: 4567 fileinfo: /style/images/fileicons/application_pdf.png;/4567/1/miravet_rodriguez_arxiv_05.pdf full_text_status: public importid: ~ institution: ~ isbn: ~ ispublished: pub issn: ~ item_issues_comment: [] item_issues_count: 0 item_issues_description: [] item_issues_id: [] item_issues_reported_by: [] item_issues_resolved_by: [] item_issues_status: [] item_issues_timestamp: [] item_issues_type: [] keywords: 'superresolution, neural networks, image sequence processing ' lastmod: 2011-03-11 08:56:12 latitude: ~ longitude: ~ metadata_visibility: show note: ~ number: ~ pagerange: 499-506 pubdom: FALSE publication: ~ publisher: Springer-Verlag refereed: TRUE referencetext: | 1. S. Borman, R. Stevenson, “Super-resolution from image sequences-a review”, Midwest Symposium on Circuits and Systems (1998). 2. S. C. Park, M. K. Park, M. G. Kang, “Super-resolution image reconstruction: a technical overview”, IEEE Signal Processing Magazine, 21-35 (2003). 3. R.Y.Tsai and T.S.Huang (Ed.), “Multiframe image restoration and registration”, in Ad-vances in Computer Vision and Image Processing volume 1, pages 317-339, JAI Press Inc.(1984). 4. R. C. Hardie, K. J. Barnard, J. G. Bognar, E. E. Armstrong, and E. A. Watson, "High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system", Optical Engineering, 37(1), 247-260 (1998). 5. R. R. Schultz and R.L. Stevenson. “Extraction of high-resolution frames from video se-quences”, IEEE Trans. Image Processing, vol. 5, nº 6, pp. 996-1011 (1996). 6. C. Miravet and F. B. Rodríguez, “A hybrid MLP-PNN architecture for fast image super-resolution”, Joint 13th International Conference on Artificial Neural Networks / 10th In-ternational Conference on Neural Information Processing (ICANN/ICONIP), Lecture notes in Computer Science, vol. 2714, Springer-Verlag, pp. 417-424 (2003). 7. C. Miravet and F. B. Rodríguez, “A two-step neural network based algorithm for fast high-resolution image reconstruction”, Image and Vision Computing (submitted). 8. M.Irani, S.Peleg, “Improving resolution by image registration”, CVGIP: Graphical Mod-els and Image Processing. 53, 231-239 (1991). 9. W. Press, S. Teukolsky, W. Vetterling, B. Flannery, Numerical recipes in C, Cambridge University Press, 2º Ed. (1992) 10. M. S. Alam, J. G. Bognar, R. C. Hardie and B. J. Yasuada, “Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames”, IEEE Transactions on instrumentation and measurement, vol. 49, nº 5 (2000). 11. A.Bishop, Neural Networks for Pattern Recognition. Oxford University Press (1995). 12. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis. Wiley-InterScience (2001). relation_type: [] relation_uri: [] reportno: ~ rev_number: 12 series: ~ source: ~ status_changed: 2007-09-12 17:00:51 subjects: - comp-sci-mach-vis - comp-sci-neural-nets succeeds: ~ suggestions: ~ sword_depositor: ~ sword_slug: ~ thesistype: ~ title: 'Accurate and robust image superresolution by neural processing of local image representations ' type: confpaper userid: 5905 volume: 3696