Digital Holography and Digital Image Processing

Yaroslavsky, Leonid P.; Iaroslavskii, L. P.

doi:10.1007/978-1-4757-4988-5

Cited by 102 publications

(111 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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]. Figure 1(a) presents a frame extracted from a real-life turbulent degraded video sequence [22], while figure (b) is the reference frame computed by applying element-wise temporal median filtering over 117 frames.…”

Section: Estimation Of the Stable Scenementioning

confidence: 99%

“…In the case where no data is available at all for a certain pixel, the interpolated stable scene estimate is used as approximation of the super-resolved image. For image interpolation discrete sinc-interpolation, as the numerically optimal interpolation method, is used [30,31,32,33]. Figure 2(a) shows the super-resolved frame generated from a real-life turbulent degraded sequence (see Figure 1(a) and [22]).…”

Section: Accumulation Of Background or Stationary Pixels' Informationmentioning

confidence: 99%

See 1 more Smart Citation

Real-time stabilization of long range observation system turbulent video

Fishbain

Yaroslavsky²,

Ideses³

2007

J Real-Time Image Proc

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

Section: Estimation Of the Stable Scenementioning

confidence: 99%

Section: Accumulation Of Background or Stationary Pixels' Informationmentioning

confidence: 99%

Real-time stabilization of long range observation system turbulent video

Fishbain

Yaroslavsky²,

Ideses³

2007

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…How can one imitate physical reality of optical signals and transforms in computers? Two principles lie in the base of digital representation of continuous signal transformations: the conformity principle with digital representation of signals and the mutual correspondence principle between continuous and discrete transformations (see [9]). The conformity principle requires that digital representation of signal transformations should parallel that of signals.…”

Section: Discrete Representation Of Transforms: Principlesmentioning

confidence: 99%

“…Shifted DFTs (SDFTs(u, v)) are obtained (see [9,10]). The "cardinal" sampling relationship (3.22b) between object signal and its Fourier spectrum sampling intervals Δx (r) and Δ f (s) is also assumed here.…”

Section: D Direct and Inverse Shifted Dfts: Discrete Cosine And Cosimentioning

confidence: 99%