Real time stabilization of long range observation system turbulent video

Fishbain, Barak; Yaroslavsky, Leonid P.; Ideses, Ianir A.; Ben-Zvi, Ofer; Shtern, Alon

doi:10.1117/12.703440

Cited by 10 publications

(19 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As our experiments show, interframe motion can span a significant portion of the field of view (FOV). This is much larger than typically considered in atmospheric turbulence [19], where local optical flow is used.…”

Section: Introductionmentioning

confidence: 99%

Detecting Motion through Dynamic Refraction

Alterman

Schechner

Perona

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

Abstract-Refraction causes random dynamic distortions in atmospheric turbulence and in views across a water interface. The latter scenario is experienced by submerged animals seeking to detect prey or avoid predators, which may be airborne or on land. Man encounters this when surveying a scene by a submarine or divers while wishing to avoid the use of an attention-drawing periscope. The problem of inverting random refracted dynamic distortions is difficult, particularly when some of the objects in the field of view (FOV) are moving. On the other hand, in many cases, just those moving objects are of interest, as they reveal animal, human, or machine activity. Furthermore, detecting and tracking these objects does not necessitate handling the difficult task of complete recovery of the scene. We show that moving objects can be detected very simply, with low false-positive rates, even when the distortions are very strong and dominate the object motion. Moreover, the moving object can be detected even if it has zero mean motion. While the object and distortion motions are random and unknown, they are mutually independent. This is expressed by a simple motion feature which enables discrimination of moving object points versus the background.

show abstract

Section: Introductionmentioning

confidence: 99%

Detecting Motion through Dynamic Refraction

Alterman

Schechner

Perona

et al. 2013

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…Hence a Real Motion Separation Mask ( ) has to be formed for each pixel in each incoming frame. To this end, a real-time two-stage decision mechanism is suggested [4]. The first step is aimed at extracting areas, such as background, that are most definitely stationary.…”

Section: Motion Discriminationmentioning

confidence: 99%

“…The use of rank smoothing filters such as median and alpha-trimmed mean secures that estimation of the pixel's real value (if there were no turbulence) is close to the mean of the array of the same pixel's values in a long period of time [19], and at the same time that moving objects, whose pixel gray levels form the tails of the gray level distribution in a long sequence, will be eliminated from the estimation. More general elementwise rank filtering in the time sequence to obtain the reference image is used in [1][2][3][4]. The suggested method utilizes pixel-wise temporal median filter for extraction of the reference stable frame [20,21].…”

Section: Estimation Of the Stable Scenementioning

confidence: 99%

“…This stage uses computing and statistical analysis of optical flow for motion segmentation ( [1][2][3][4]). Cluster analysis of the Magnitude distribution function for all (x, y), in a particular frame, allows separating two types of motion amplitudes: small and irregular and large and regular.…”

Section: Motion Discriminationmentioning

confidence: 99%

“…Recently, a turbulence compensation algorithm applying local neighborhood methods was introduced suggested [1,2,3,4]. The algorithm uses, for reconstructing distortion-compensated image frames, an adaptive control grid interpolation method based on estimating the local spatial displacement vectors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time stabilization of long range observation system turbulent video

Fishbain

Yaroslavsky²,

Ideses³

2007

J Real-Time Image Proc

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Yaroslavsky¹

2015

J. Opt.

View full text Add to dashboard Cite

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, in image resampling and geometrical transformations and can be traced back to early 1970-ths. The paper presents a review of these methods with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive transform domain filters for image restoration, to methods of precise image resampling and image reconstruction from sparse samples and up to "compressive sensing" approach that has gained popularity in last few years. The review has a tutorial character and purpose.

show abstract

Real time stabilization of long range observation system turbulent video

Cited by 10 publications

References 0 publications

Detecting Motion through Dynamic Refraction

Detecting Motion through Dynamic Refraction

Real-time stabilization of long range observation system turbulent video

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Contact Info

Product

Resources

About