Motion deblurring: algorithms and systems
A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms an...
Gespeichert in:
| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2014
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| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xiv, 293 pages) |
| ISBN: | 9781107360181 |
| DOI: | 10.1017/CBO9781107360181 |
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| 245 | 1 | 0 | |a Motion deblurring |b algorithms and systems |c edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park |
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| 505 | 8 | |a Machine generated contents note: 1. Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia -- 1.1. Non-blind deconvolution -- 1.2. Blind deconvolution -- 2. Spatially-varying image deblurring / Richard Szeliski -- 2.1. Review of image deblurring methods -- 2.2.A unified camera-shake blur model -- 2.3. Single image deblurring using motion density functions -- 2.4. Image deblurring using inertial measurement sensors -- 2.5. Generating sharp panoramas from motion-blurred videos -- 2.6. Discussion -- 3. Hybrid-imaging for motion deblurring / Shree K. Nayar -- 3.1. Introduction -- 3.2. Fundamental resolution tradeoff -- 3.3. Hybrid-imaging systems -- 3.4. Shift-invariant PSF image deblurring -- 3.5. Spatially-varying PSF image deblurring -- 3.6. Moving object deblurring -- 3.7. Discussion and summary -- 4. Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce -- 4.1. Introduction -- 4.2. Modelling spatially-variant camera-shake blur | |
| 505 | 8 | |a Contents note continued: 13.8. Summary and discussion | |
| 505 | 8 | |a Contents note continued: 11. Coded exposure motion deblurring for recognition / Scott McCloskey -- 11.1. Motion sensitivity of iris recognition -- 11.2. Coded exposure -- 11.3. Coded exposure performance on iris recognition -- 11.4. Barcodes -- 11.5. More general subject motion -- 11.6. Implications of computational imaging for recognition -- 11.7. Conclusion -- 12. Direct recognition of motion-blurred faces / Rama Chellappa -- 12.1. Introduction -- 12.2. The set of all motion-blurred images -- 12.3. Bank of classifiers approach for recognizing motion-blurred faces -- 12.4. Experimental evaluation -- 12.5. Discussion -- 13. Performance limits for motion deblurring cameras / Mohit Gupta -- 13.1. Introduction -- 13.2. Performance bounds for flutter shutter cameras -- 13.3. Performance bound for motion-invariant cameras -- 13.4. Simulations to verify performance bounds -- 13.5. Role of image priors -- 13.6. When to use computational imaging -- 13.7. Relationship to other computational imaging systems | |
| 505 | 8 | |a Contents note continued: 7.7. Optimized codes for PSF estimation -- 7.8. Implementation -- 7.9. Analysis -- 7.10. Summary -- 8. Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown -- 8.1. Introduction -- 8.2. Related work -- 8.3. The projective motion blur model -- 8.4. Projective motion Richardson--Lucy -- 8.5. Motion estimation -- 8.6. Experiment results -- 8.7. Discussion and conclusion -- 9. HDR imaging in the presence of motion blur / A.N. Rajagopalan -- 9.1. Introduction -- 9.2. Existing approaches to HDRJ -- 9.3. CRF, irradiance estimation, and tone-mapping -- 9.4. HDR imaging under uniform blurring -- 9.5. HDRI for non-uniform blurring -- 9.6. Experimental results -- 9.7. Conclusions and discussions -- 10.Compressive video sensing to tackle motion blur / Dikpal Reddy -- 10.1. Introduction -- 10.2. Related work -- 10.3. Imaging architecture -- 10.4. High-speed video recovery -- 10.5. Experimental results -- 10.6. Conclusions | |
| 505 | 8 | |a Contents note continued: 4.3. The computational model -- 4.4. Blind estimation of blur from a single image -- 4.5. Efficient computation of the spatially-variant model -- 4.6. Single-image deblurring results -- 4.7. Implementation -- 4.8. Conclusion -- 5. Removing camera shake in smartphones without hardware stabilization / Jan Flusser -- 5.1. Introduction -- 5.2. Image acquisition model -- 5.3. Inverse problem -- 5.4. Pinhole camera model -- 5.5. Smartphone application -- 5.6. Evaluation -- 5.7. Conclusions -- 6. Multi-sensor fusion for motion deblurring / Jlngyi Yu -- 6.1. Introduction -- 6.2. Hybrid-speed sensor -- 6.3. Motion deblurring -- 6.4. Depth map super-resolution -- 6.5. Extensions to low-light imaging -- 6.6. Discussion and summary -- 7. Motion deblurring using fluttered shutter / Amit Agrawal -- 7.1. Related work -- 7.2. Coded exposure photography -- 7.3. Image deconvolution -- 7.4. Code selection -- 7.5. Linear solution for deblurring -- 7.6. Resolution enhancement | |
| 520 | |a A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields | ||
| 650 | 4 | |a Datenverarbeitung | |
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| 650 | 4 | |a Image processing / Digital techniques | |
| 650 | 4 | |a Digital video / Editing / Mathematics | |
| 650 | 4 | |a Photography / Retouching / Data processing | |
| 650 | 4 | |a Motion / Mathematical models | |
| 700 | 1 | |a Rajagopalan, A. N. |4 edt | |
| 700 | 1 | |a Chellappa, Rama |4 edt | |
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Datensatz im Suchindex
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| contents | Machine generated contents note: 1. Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia -- 1.1. Non-blind deconvolution -- 1.2. Blind deconvolution -- 2. Spatially-varying image deblurring / Richard Szeliski -- 2.1. Review of image deblurring methods -- 2.2.A unified camera-shake blur model -- 2.3. Single image deblurring using motion density functions -- 2.4. Image deblurring using inertial measurement sensors -- 2.5. Generating sharp panoramas from motion-blurred videos -- 2.6. Discussion -- 3. Hybrid-imaging for motion deblurring / Shree K. Nayar -- 3.1. Introduction -- 3.2. Fundamental resolution tradeoff -- 3.3. Hybrid-imaging systems -- 3.4. Shift-invariant PSF image deblurring -- 3.5. Spatially-varying PSF image deblurring -- 3.6. Moving object deblurring -- 3.7. Discussion and summary -- 4. Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce -- 4.1. Introduction -- 4.2. Modelling spatially-variant camera-shake blur Contents note continued: 13.8. Summary and discussion Contents note continued: 11. Coded exposure motion deblurring for recognition / Scott McCloskey -- 11.1. Motion sensitivity of iris recognition -- 11.2. Coded exposure -- 11.3. Coded exposure performance on iris recognition -- 11.4. Barcodes -- 11.5. More general subject motion -- 11.6. Implications of computational imaging for recognition -- 11.7. Conclusion -- 12. Direct recognition of motion-blurred faces / Rama Chellappa -- 12.1. Introduction -- 12.2. The set of all motion-blurred images -- 12.3. Bank of classifiers approach for recognizing motion-blurred faces -- 12.4. Experimental evaluation -- 12.5. Discussion -- 13. Performance limits for motion deblurring cameras / Mohit Gupta -- 13.1. Introduction -- 13.2. Performance bounds for flutter shutter cameras -- 13.3. Performance bound for motion-invariant cameras -- 13.4. Simulations to verify performance bounds -- 13.5. Role of image priors -- 13.6. When to use computational imaging -- 13.7. Relationship to other computational imaging systems Contents note continued: 7.7. Optimized codes for PSF estimation -- 7.8. Implementation -- 7.9. Analysis -- 7.10. Summary -- 8. Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown -- 8.1. Introduction -- 8.2. Related work -- 8.3. The projective motion blur model -- 8.4. Projective motion Richardson--Lucy -- 8.5. Motion estimation -- 8.6. Experiment results -- 8.7. Discussion and conclusion -- 9. HDR imaging in the presence of motion blur / A.N. Rajagopalan -- 9.1. Introduction -- 9.2. Existing approaches to HDRJ -- 9.3. CRF, irradiance estimation, and tone-mapping -- 9.4. HDR imaging under uniform blurring -- 9.5. HDRI for non-uniform blurring -- 9.6. Experimental results -- 9.7. Conclusions and discussions -- 10.Compressive video sensing to tackle motion blur / Dikpal Reddy -- 10.1. Introduction -- 10.2. Related work -- 10.3. Imaging architecture -- 10.4. High-speed video recovery -- 10.5. Experimental results -- 10.6. Conclusions Contents note continued: 4.3. The computational model -- 4.4. Blind estimation of blur from a single image -- 4.5. Efficient computation of the spatially-variant model -- 4.6. Single-image deblurring results -- 4.7. Implementation -- 4.8. Conclusion -- 5. Removing camera shake in smartphones without hardware stabilization / Jan Flusser -- 5.1. Introduction -- 5.2. Image acquisition model -- 5.3. Inverse problem -- 5.4. Pinhole camera model -- 5.5. Smartphone application -- 5.6. Evaluation -- 5.7. Conclusions -- 6. Multi-sensor fusion for motion deblurring / Jlngyi Yu -- 6.1. Introduction -- 6.2. Hybrid-speed sensor -- 6.3. Motion deblurring -- 6.4. Depth map super-resolution -- 6.5. Extensions to low-light imaging -- 6.6. Discussion and summary -- 7. Motion deblurring using fluttered shutter / Amit Agrawal -- 7.1. Related work -- 7.2. Coded exposure photography -- 7.3. Image deconvolution -- 7.4. Code selection -- 7.5. Linear solution for deblurring -- 7.6. Resolution enhancement |
| ctrlnum | (ZDB-20-CBO)CR9781107360181 (OCoLC)992881698 (DE-599)BVBBV043944666 |
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| discipline | Informatik |
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| id | DE-604.BV043944666 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:39:22Z |
| institution | BVB |
| isbn | 9781107360181 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029353637 |
| oclc_num | 992881698 |
| open_access_boolean | |
| owner | DE-12 DE-92 |
| owner_facet | DE-12 DE-92 |
| physical | 1 online resource (xiv, 293 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Motion deblurring algorithms and systems edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park Cambridge Cambridge University Press 2014 1 online resource (xiv, 293 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Machine generated contents note: 1. Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia -- 1.1. Non-blind deconvolution -- 1.2. Blind deconvolution -- 2. Spatially-varying image deblurring / Richard Szeliski -- 2.1. Review of image deblurring methods -- 2.2.A unified camera-shake blur model -- 2.3. Single image deblurring using motion density functions -- 2.4. Image deblurring using inertial measurement sensors -- 2.5. Generating sharp panoramas from motion-blurred videos -- 2.6. Discussion -- 3. Hybrid-imaging for motion deblurring / Shree K. Nayar -- 3.1. Introduction -- 3.2. Fundamental resolution tradeoff -- 3.3. Hybrid-imaging systems -- 3.4. Shift-invariant PSF image deblurring -- 3.5. Spatially-varying PSF image deblurring -- 3.6. Moving object deblurring -- 3.7. Discussion and summary -- 4. Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce -- 4.1. Introduction -- 4.2. Modelling spatially-variant camera-shake blur Contents note continued: 13.8. Summary and discussion Contents note continued: 11. Coded exposure motion deblurring for recognition / Scott McCloskey -- 11.1. Motion sensitivity of iris recognition -- 11.2. Coded exposure -- 11.3. Coded exposure performance on iris recognition -- 11.4. Barcodes -- 11.5. More general subject motion -- 11.6. Implications of computational imaging for recognition -- 11.7. Conclusion -- 12. Direct recognition of motion-blurred faces / Rama Chellappa -- 12.1. Introduction -- 12.2. The set of all motion-blurred images -- 12.3. Bank of classifiers approach for recognizing motion-blurred faces -- 12.4. Experimental evaluation -- 12.5. Discussion -- 13. Performance limits for motion deblurring cameras / Mohit Gupta -- 13.1. Introduction -- 13.2. Performance bounds for flutter shutter cameras -- 13.3. Performance bound for motion-invariant cameras -- 13.4. Simulations to verify performance bounds -- 13.5. Role of image priors -- 13.6. When to use computational imaging -- 13.7. Relationship to other computational imaging systems Contents note continued: 7.7. Optimized codes for PSF estimation -- 7.8. Implementation -- 7.9. Analysis -- 7.10. Summary -- 8. Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown -- 8.1. Introduction -- 8.2. Related work -- 8.3. The projective motion blur model -- 8.4. Projective motion Richardson--Lucy -- 8.5. Motion estimation -- 8.6. Experiment results -- 8.7. Discussion and conclusion -- 9. HDR imaging in the presence of motion blur / A.N. Rajagopalan -- 9.1. Introduction -- 9.2. Existing approaches to HDRJ -- 9.3. CRF, irradiance estimation, and tone-mapping -- 9.4. HDR imaging under uniform blurring -- 9.5. HDRI for non-uniform blurring -- 9.6. Experimental results -- 9.7. Conclusions and discussions -- 10.Compressive video sensing to tackle motion blur / Dikpal Reddy -- 10.1. Introduction -- 10.2. Related work -- 10.3. Imaging architecture -- 10.4. High-speed video recovery -- 10.5. Experimental results -- 10.6. Conclusions Contents note continued: 4.3. The computational model -- 4.4. Blind estimation of blur from a single image -- 4.5. Efficient computation of the spatially-variant model -- 4.6. Single-image deblurring results -- 4.7. Implementation -- 4.8. Conclusion -- 5. Removing camera shake in smartphones without hardware stabilization / Jan Flusser -- 5.1. Introduction -- 5.2. Image acquisition model -- 5.3. Inverse problem -- 5.4. Pinhole camera model -- 5.5. Smartphone application -- 5.6. Evaluation -- 5.7. Conclusions -- 6. Multi-sensor fusion for motion deblurring / Jlngyi Yu -- 6.1. Introduction -- 6.2. Hybrid-speed sensor -- 6.3. Motion deblurring -- 6.4. Depth map super-resolution -- 6.5. Extensions to low-light imaging -- 6.6. Discussion and summary -- 7. Motion deblurring using fluttered shutter / Amit Agrawal -- 7.1. Related work -- 7.2. Coded exposure photography -- 7.3. Image deconvolution -- 7.4. Code selection -- 7.5. Linear solution for deblurring -- 7.6. Resolution enhancement A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research. It encompasses both algorithms and architectures, providing detailed coverage of practical techniques by leading researchers. From an algorithms perspective, blind and non-blind approaches are discussed, including the use of single or multiple images; projective motion blur model; image priors and parametric models; high dynamic range imaging in the irradiance domain; and image recognition in blur. Performance limits for motion deblurring cameras are also presented. From a systems perspective, hybrid frameworks combining low-resolution-high-speed and high-resolution-low-speed cameras are described, along with the use of inertial sensors and coded exposure cameras. Also covered is an architecture exploiting compressive sensing for video recovery. A valuable resource for researchers and practitioners in computer vision, image processing, and related fields Datenverarbeitung Mathematik Mathematisches Modell Image processing / Mathematics Image processing / Digital techniques Digital video / Editing / Mathematics Photography / Retouching / Data processing Motion / Mathematical models Rajagopalan, A. N. edt Chellappa, Rama edt Erscheint auch als Druckausgabe 978-1-107-04436-4 Erscheint auch als Druckausgabe 978-1-107-62149-7 https://doi.org/10.1017/CBO9781107360181 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Motion deblurring algorithms and systems Machine generated contents note: 1. Mathematical models and practical solvers for uniform motion deblurring / Jiaya Jia -- 1.1. Non-blind deconvolution -- 1.2. Blind deconvolution -- 2. Spatially-varying image deblurring / Richard Szeliski -- 2.1. Review of image deblurring methods -- 2.2.A unified camera-shake blur model -- 2.3. Single image deblurring using motion density functions -- 2.4. Image deblurring using inertial measurement sensors -- 2.5. Generating sharp panoramas from motion-blurred videos -- 2.6. Discussion -- 3. Hybrid-imaging for motion deblurring / Shree K. Nayar -- 3.1. Introduction -- 3.2. Fundamental resolution tradeoff -- 3.3. Hybrid-imaging systems -- 3.4. Shift-invariant PSF image deblurring -- 3.5. Spatially-varying PSF image deblurring -- 3.6. Moving object deblurring -- 3.7. Discussion and summary -- 4. Efficient, blind, spatially-variant deblurring for shaken images / Jean Ponce -- 4.1. Introduction -- 4.2. Modelling spatially-variant camera-shake blur Contents note continued: 13.8. Summary and discussion Contents note continued: 11. Coded exposure motion deblurring for recognition / Scott McCloskey -- 11.1. Motion sensitivity of iris recognition -- 11.2. Coded exposure -- 11.3. Coded exposure performance on iris recognition -- 11.4. Barcodes -- 11.5. More general subject motion -- 11.6. Implications of computational imaging for recognition -- 11.7. Conclusion -- 12. Direct recognition of motion-blurred faces / Rama Chellappa -- 12.1. Introduction -- 12.2. The set of all motion-blurred images -- 12.3. Bank of classifiers approach for recognizing motion-blurred faces -- 12.4. Experimental evaluation -- 12.5. Discussion -- 13. Performance limits for motion deblurring cameras / Mohit Gupta -- 13.1. Introduction -- 13.2. Performance bounds for flutter shutter cameras -- 13.3. Performance bound for motion-invariant cameras -- 13.4. Simulations to verify performance bounds -- 13.5. Role of image priors -- 13.6. When to use computational imaging -- 13.7. Relationship to other computational imaging systems Contents note continued: 7.7. Optimized codes for PSF estimation -- 7.8. Implementation -- 7.9. Analysis -- 7.10. Summary -- 8. Richardson-Lucy deblurring for scenes under a projective motion path / Michael S. Brown -- 8.1. Introduction -- 8.2. Related work -- 8.3. The projective motion blur model -- 8.4. Projective motion Richardson--Lucy -- 8.5. Motion estimation -- 8.6. Experiment results -- 8.7. Discussion and conclusion -- 9. HDR imaging in the presence of motion blur / A.N. Rajagopalan -- 9.1. Introduction -- 9.2. Existing approaches to HDRJ -- 9.3. CRF, irradiance estimation, and tone-mapping -- 9.4. HDR imaging under uniform blurring -- 9.5. HDRI for non-uniform blurring -- 9.6. Experimental results -- 9.7. Conclusions and discussions -- 10.Compressive video sensing to tackle motion blur / Dikpal Reddy -- 10.1. Introduction -- 10.2. Related work -- 10.3. Imaging architecture -- 10.4. High-speed video recovery -- 10.5. Experimental results -- 10.6. Conclusions Contents note continued: 4.3. The computational model -- 4.4. Blind estimation of blur from a single image -- 4.5. Efficient computation of the spatially-variant model -- 4.6. Single-image deblurring results -- 4.7. Implementation -- 4.8. Conclusion -- 5. Removing camera shake in smartphones without hardware stabilization / Jan Flusser -- 5.1. Introduction -- 5.2. Image acquisition model -- 5.3. Inverse problem -- 5.4. Pinhole camera model -- 5.5. Smartphone application -- 5.6. Evaluation -- 5.7. Conclusions -- 6. Multi-sensor fusion for motion deblurring / Jlngyi Yu -- 6.1. Introduction -- 6.2. Hybrid-speed sensor -- 6.3. Motion deblurring -- 6.4. Depth map super-resolution -- 6.5. Extensions to low-light imaging -- 6.6. Discussion and summary -- 7. Motion deblurring using fluttered shutter / Amit Agrawal -- 7.1. Related work -- 7.2. Coded exposure photography -- 7.3. Image deconvolution -- 7.4. Code selection -- 7.5. Linear solution for deblurring -- 7.6. Resolution enhancement Datenverarbeitung Mathematik Mathematisches Modell Image processing / Mathematics Image processing / Digital techniques Digital video / Editing / Mathematics Photography / Retouching / Data processing Motion / Mathematical models |
| title | Motion deblurring algorithms and systems |
| title_auth | Motion deblurring algorithms and systems |
| title_exact_search | Motion deblurring algorithms and systems |
| title_full | Motion deblurring algorithms and systems edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park |
| title_fullStr | Motion deblurring algorithms and systems edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park |
| title_full_unstemmed | Motion deblurring algorithms and systems edited by A.N. Rajagopalan, Indian Institute of Technology, Madras, Rama Chellappa, University of Maryland, College Park |
| title_short | Motion deblurring |
| title_sort | motion deblurring algorithms and systems |
| title_sub | algorithms and systems |
| topic | Datenverarbeitung Mathematik Mathematisches Modell Image processing / Mathematics Image processing / Digital techniques Digital video / Editing / Mathematics Photography / Retouching / Data processing Motion / Mathematical models |
| topic_facet | Datenverarbeitung Mathematik Mathematisches Modell Image processing / Mathematics Image processing / Digital techniques Digital video / Editing / Mathematics Photography / Retouching / Data processing Motion / Mathematical models |
| url | https://doi.org/10.1017/CBO9781107360181 |
| work_keys_str_mv | AT rajagopalanan motiondeblurringalgorithmsandsystems AT chellapparama motiondeblurringalgorithmsandsystems |