Recent Advances in Space-Variant Deblurring and Image Stabilization

The blur caused by camera motion is a serious problem in many areas of optical imaging such as remote sensing, aerial reconnaissance or digital photogra phy. As a rule, this problem occurs when low ambient light conditions prevent an imaging system from using sufficiently short exposure times, resulting in a blurred image due to the relative motion between a scene and the imaging system. For exam ple, the cameras attached to airplanes and helicopters are blurred by the forward motion of the aircraft and vibrations. Similarly when taking photographs by hand under dim lighting conditions, camera shake leads to objectionable blur. Producers of imaging systems introduce compensation mechanisms such as gyroscope gim bals in the case of aerial sensing or optical image stabilization systems in the case of digital cameras. These solutions partially remove the blur at the expense of higher cost, weight and energy consumption. Recent advances in image processing make it possible to remove the blur in software. This chapter reviews the image processing techniques we can use for this purpose, discusses the achievable performance and presents some promising results achieved by the authors.

[1]  Jan Flusser,et al.  Space-Variant Restoration of Images Degraded by Camera Motion Blur , 2008, IEEE Transactions on Image Processing.

[2]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[3]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[4]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[5]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..

[6]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[7]  Andrew Zisserman,et al.  Multiple View Geometry in Computer Vision (2nd ed) , 2003 .

[8]  Jian Sun,et al.  Progressive inter-scale and intra-scale non-blind image deconvolution , 2008, SIGGRAPH 2008.

[9]  N. Kehtarnavaz,et al.  Imaeg Blur Reduction for Cell-Phone Cameras Via Adaptive Tonal Correction , 2007, 2007 IEEE International Conference on Image Processing.

[10]  M. Girolami,et al.  Advances in Independent Component Analysis , 2000, Perspectives in Neural Computing.

[11]  Marius Tico,et al.  Motion Blur Identification Based on Differently Exposed Images , 2006, 2006 International Conference on Image Processing.

[12]  Jan Flusser,et al.  Multichannel blind deconvolution of spatially misaligned images , 2005, IEEE Transactions on Image Processing.

[13]  David J. C. MacKay,et al.  Ensemble Learning for Blind Image Separation and Deconvolution , 2000 .

[14]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .

[15]  Rachid Deriche,et al.  Vector-valued image regularization with PDEs: a common framework for different applications , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[17]  E. Oja,et al.  Independent Component Analysis , 2001 .