Suppression of atmospheric turbulence in video using an adaptive control grid interpolation approach

Monaco, Joseph W.; Smith, M.J.T.

doi:10.1109/icassp.2001.941311

Cited by 35 publications

(26 citation statements)

References 8 publications

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“…A number of investigators have recognized this and proposed methods for suppressing turbulent motion. In particular, motion compensation techniques were proposed by a few authors 8,9 to address the geometric distortion component D. In their work, reference frames are first formed by time-averaging the degraded video frames. Then, the current frames are registered toward the reference frames.…”

Section: Introductionmentioning

confidence: 99%

<title>An adaptive model for restoration of optically distorted video frames</title>

Smith

Mersereau

2006

SPIE Proceedings

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Atmospheric turbulence is a common problem in astronomy and long distance surveillance applications. It can lead to optical distortions that can significantly degrade the quality of the captured images and video. Quality improvement can be achieved through digital restoration methods that effectively suppress the effects of optical distortion. In this paper, atmospheric optical distortion is modeled as having two components: a dispersive component and a time-varying distortion component. A new restoration algorithm is introduced that compensates for dispersion using a fourth-order statistic and employs a new adaptive warping algorithm to suppress turbulent motion effects. The new algorithm is able to improve quality significantly and is able to handle difficult cases involving panning, zooming, and natural motion.

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Section: Introductionmentioning

confidence: 99%

<title>An adaptive model for restoration of optically distorted video frames</title>

Smith

Mersereau

2006

SPIE Proceedings

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“…The principal idea is to use, for reconstructing distortion-compensated image frames, an adaptively controlled image resampling method based on the estimate of image local displacement vectors. Using those concepts, turbulence compensation algorithms which preserve genuine motion in the scene are suggested in [43][44][45][46][47].…”

Section: Channel Characterization and Processing Principlesmentioning

confidence: 99%

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Fishbain

Yaroslavsky

Ideses

2008

Advances in Optical Technologies

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This paper presents methods for intrachannel and interchannel fusion of thermal and visual sensors used in long-distance terrestrial observation systems. Intrachannel spatial and temporal fusion mechanisms used for image stabilization, super-resolution, denoising, and deblurring are supplemented by interchannel data fusion of visual-and thermal-range channels for generating fused videos intended for visual analysis by a human operator. Tests on synthetic, as well as on real-life, video sequences have confirmed the potential of the suggested methods.

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“…The centroid method described in the present article is intended to find, or at least approximate, the vector fields u i from the sequence of images I i . The centroid method for the correction of turbulence was introduced, in a slightly informal way, by Frakes-Monaco-Smith in 2001 [5] and it was formalized recently by Micheli [15]. There are other methods for the correction of turbulence based on optical flow [13].…”

Section: Introductionmentioning

confidence: 99%

“…A first method consists in computing the average of all the input images I i , which converges to the image I blurred by the positive kernel κ, and then de-convolve the average image [6,7,9,16]. A second method consists in inverting the deformation of one of the images I i to recover the image I [5,4,21,8,15]. In the first case, one must know exactly the statistical distribution κ of the fields u i and have a large enough set of independent images I i .…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Centroid Method for the Correction of Turbulence

Meinhardt-Llopis

Micheli

2014

Image Processing on Line

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The centroid method for the correction of turbulence consists in computing the Karcher-Fréchet mean of the sequence of input images. The direction of deformation between a pair of images is determined by the optical flow. A distinguishing feature of the centroid method is that it can produce useful results from an arbitrarily small set of input images. Source CodeThe source code and a online demo are accessible at the IPOL web page of this article 1 .

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Suppression of atmospheric turbulence in video using an adaptive control grid interpolation approach

Cited by 35 publications

References 8 publications

<title>An adaptive model for restoration of optically distorted video frames</title>

<title>An adaptive model for restoration of optically distorted video frames</title>

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Implementation of the Centroid Method for the Correction of Turbulence

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