Removing Atmospheric Turbulence via Space-Invariant Deconvolution

To correct geometric distortion and reduce space and time-varying blur, a new approach is proposed in this paper capable of restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence. This approach reduces the space and time-varying deblurring problem to a shift invariant one. It first registers each frame to suppress geometric deformation through B-spline-based nonrigid registration. Next, a temporal regression process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant near-diffraction-limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a final output. Experiments using real data illustrate that this approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality.

[1]  Michael F. Cohen,et al.  Seeing Mt. Rainier: Lucky imaging for multi-image denoising, sharpening, and haze removal , 2010, 2010 IEEE International Conference on Computational Photography (ICCP).

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

[3]  Mikhail A. Vorontsov Parallel image processing based on an evolution equation with anisotropic gain: integrated optoelectronic architectures , 1999 .

[4]  D. Fried Probability of getting a lucky short-exposure image through turbulence* , 1978 .

[5]  Michael C. Roggemann,et al.  Image-spectrum signal-to-noise-ratio improvements by statistical frame selection for adaptive-optics imaging through atmospheric turbulence , 1994 .

[6]  Robert K. Tyson Principles of Adaptive Optics , 1991 .

[7]  Nicholas M. Law,et al.  Lucky imaging: diffraction-limited astronomy from the ground in the visible , 2007 .

[8]  Xiang Zhu,et al.  Stabilizing and deblurring atmospheric turbulence , 2011, 2011 IEEE International Conference on Computational Photography (ICCP).

[9]  Azeddine Beghdadi,et al.  A New Look to Multichannel Blind Image Deconvolution , 2009, IEEE Transactions on Image Processing.

[10]  Xiang Zhu,et al.  Image reconstruction from videos distorted by atmospheric turbulence , 2010, Electronic Imaging.

[11]  Richard Szeliski,et al.  Spline-Based Image Registration , 1997, International Journal of Computer Vision.

[12]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[13]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

[14]  Ali R. Khan,et al.  Symmetric Data Attachment Terms for Large Deformation Image Registration , 2007, IEEE Transactions on Medical Imaging.

[15]  Scott W. Teare,et al.  Ground-based High-Resolution Imaging of Mercury , 2000 .

[16]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[17]  S. John,et al.  Multiframe selective information fusion from robust error estimation theory , 2005, IEEE Transactions on Image Processing.

[18]  Mubarak Shah,et al.  A two-stage reconstruction approach for seeing through water , 2011, CVPR 2011.

[19]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[20]  Bernhard Schölkopf,et al.  Efficient filter flow for space-variant multiframe blind deconvolution , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  W. Middleton,et al.  Vision Through the Atmosphere , 1952 .

[22]  Yuandong Tian,et al.  Seeing through water: Image restoration using model-based tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[23]  Mikhail A. Vorontsov,et al.  Image enhancement by local information fusion with pre-processing and composed metric , 2008, Optical Engineering + Applications.

[24]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, CVPR.

[25]  Russell B. Makidon,et al.  Strehl ratio and image sharpness for adaptive optics , 2006, SPIE Astronomical Telescopes + Instrumentation.

[26]  Masatoshi Okutomi,et al.  Super-resolution from image sequence under influence of hot-air optical turbulence , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Steven J. Simske,et al.  Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis , 2007, IEEE Geoscience and Remote Sensing Letters.

[28]  Mikhail A. Vorontsov,et al.  Automated video enhancement from a stream of atmospherically-distorted images: the lucky-region fusion approach , 2009, Optical Engineering + Applications.

[29]  Robert N. Tubbs,et al.  Lucky Exposures:: Diffraction Limited Astronomical Imaging through the Atmosphere , 2003 .

[30]  Suvrit Sra,et al.  Online blind deconvolution for astronomical imaging , 2009, 2009 IEEE International Conference on Computational Photography (ICCP).

[31]  B. Welsh,et al.  Imaging Through Turbulence , 1996 .

[32]  F. Hampel The Influence Curve and Its Role in Robust Estimation , 1974 .

[33]  M A Vorontsov,et al.  Anisoplanatic imaging through turbulent media: image recovery by local information fusion from a set of short-exposure images. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[34]  S. Farsiu,et al.  Constrained, globally optimal, multi-frame motion estimation , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[35]  Laurie Davies,et al.  The identification of multiple outliers , 1993 .