Generation of a restored image from a video sequence recorded under turbulence effects

Cohen, Boaz; Avrin, Vadim; Belitsky, Moshe; Dinstein, I.

doi:10.1117/1.601592

Cited by 8 publications

(6 citation statements)

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“…Averaging several line responses along the edge ͑for noise reduction͒ produces an estimate of the line spread function ͑LSF͒, whose Fourier transform is the estimated overall MTF ͑assuming isotropic degradation properties͒. [21][22][23] The estimated MTF was then used to restore the whole image set in the video clip, according to Eq. ͑2͒.…”

Section: Restoration Of the Atmospherically Degradedmentioning

confidence: 99%

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Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere

et al. 2006

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Remotely sensed videos, captured by high-resolution imagers, are likely to be degraded by the atmosphere. In still images, the degradation sources, which include turbulence and aerosols, mainly cause blur. In video sequences, however, spatiotemporally varying distortions caused by turbulence also become important. These atmospheric degradations reduce image quality and therefore the ability of target acquisition by the observers. The effects of image quality and image restoration ͑deblurring͒ on target acquisition in still images were examined previously in several studies. Nevertheless, results obtained in static situations may not be appropriate for dynamic situations ͑with moving targets͒, which are frequently more realistic. This work examines the effect of image restoration on the ability of observers to acquire moving objects ͑such as humans and vehicles͒ in video sequences. This is done through perception experiments that compare acquisition probabilities in both restored and nonrestored video sequences captured by a remotesensing thermal imaging system. Results show that image restoration can significantly improve the acquisition probability. These results correspond to the static case. However, unlike the static case, considerably smaller differences were obtained here between the probabilities of target detection and target recognition. © 2006 Society of Photo-Optical Instrumentation Engineers.

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Section: Restoration Of the Atmospherically Degradedmentioning

confidence: 99%

“…25 ͑atmospheric-dominated͒ MTF required for the restoration filtering process was obtained for each clip using a stepedge response extracted from a static object. [21][22][23] The blur resulting from the motion of the moving objects themselves was neglected because their velocity in the image plane was low.…”

Section: Introductionmentioning

confidence: 99%

Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere

et al. 2006

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“…One particularly simple case is a sequence of a still scene taken under time-varying degradations, such as random turbulence or noisy conditions. The effects of turbulence 1 or noise 2,3 can be diminished by simply averaging several frames, which tends to cancel the random variations.…”

Section: Introductionmentioning

confidence: 99%

Automatic enhancement of noisy image sequences through local spatiotemporal spectrum analysis

Nestares¹

2000

Opt. Eng

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A fully automatic method is proposed to produce an enhanced image from a very noisy sequence consisting of a translating object over a background with a different translational motion. The method is based on averaging registered versions of the frames in which the object has been motion-compensated. Conventional techniques for displacement estimation are not adequate for these very noisy sequences, and thus a new strategy has been used, taking advantage of a simple model of the sequences. First, the local spatiotemporal spectrum is estimated through a bank of multidirectional, multiscale third-order Gaussian derivative filters, yielding a representation of the sequence that facilitates further processing and analysis tasks. Then, energy-related measurements describing the local texture and motion are easily extracted from this representation. These descriptors are used to segment the sequence according to a local joint measure of motion and texture. Once the object of interest has been segmented, its velocity is estimated applying the gradient constraint to the output of a directional bandpass filter for all pixels belonging to the object. Velocity estimates are then used to compensate the motion prior to the average. The results obtained with real sequences of moving ships taken under very noisy conditions are highly satisfactory, demonstrating the robustness and usefulness of the proposed method.

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“…Such an image has to be obtained from the input sequence itself [17,18,19]. In order to achieve optimal results, the reference image should have the following properties:…”

Section: Estimation Of the Stable Scenementioning

confidence: 99%

Real-time stabilization of long range observation system turbulent video

Fishbain

Yaroslavsky²,

Ideses³

2007

J Real-Time Image Proc

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Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a "reference" frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.

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Generation of a restored image from a video sequence recorded under turbulence effects

Cited by 8 publications

References 0 publications

Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere

Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere

Automatic enhancement of noisy image sequences through local spatiotemporal spectrum analysis

Real-time stabilization of long range observation system turbulent video

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