Spatial coherence model for passive ranging using distributed arrays

Off-axis electron holography has the well known ability to preserve the complex image wave within the final, recorded image. This final image described by I(x,y) = I(r) contains contributions from the image intensity of the elastically scattered electrons IeI (r) = |A(r) exp (iΦ(r)) |, the contributions from the inelastically scattered electrons IineI (r), and the complex image wave Ψ = A(r) exp(iΦ(r)) as:(1) I(r) = IeI (r) + Iinel (r) + μ A(r) cos(2π Δk r + Φ(r))where the constant μ describes the contrast of the interference fringes which are related to the spatial coherence of the electron beam, and Φk is the resulting vector of the difference of the wavefront vectors of the two overlaping beams. Using a software package like HoloWorks, the complex image wave Ψ can be extracted.

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The influence of temporal and spatial coherence on object representations

PsycEXTRA Dataset ◽

10.1037/e536982012-018 ◽

1997 ◽

Author(s):

Ken Seergobin ◽

Steve Joordens

Keyword(s):

Spatial Coherence ◽

Object Representations ◽

Temporal And Spatial

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ANALYSIS OF MODIFICATION OF THE ENERGY METHOD OF PASSIVE RANGING

Автометрия ◽

10.15372/aut20160105 ◽

2016 ◽

Vol 52 (1) ◽

Keyword(s):

Energy Method ◽

Passive Ranging

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An Alternate Algorithm for (3x3) Median Filtering of Digital Images

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v1i1.6732 ◽

2012 ◽

Vol 2 (1) ◽

pp. 7-9 ◽

Cited By ~ 2

Author(s):

Satinderjit Singh

Keyword(s):

Median Filter ◽

Computational Cost ◽

Spatial Coherence ◽

General Purpose ◽

Median Filtering ◽

Basic Algorithm ◽

Temporal Complexity ◽

Filter Kernel ◽

One Step ◽

High Computational Cost

Median filtering is a commonly used technique in image processing. The main problem of the median filter is its high computational cost (for sorting N pixels, the temporal complexity is O(NÂ·log N), even with the most efficient sorting algorithms). When the median filter must be carried out in real time, the software implementation in general-purpose processorsdoes not usually give good results. This Paper presents an efficient algorithm for median filtering with a 3x3 filter kernel with only about 9 comparisons per pixel using spatial coherence between neighboring filter computations. The basic algorithm calculates two medians in one step and reuses sorted slices of three vertical neighboring pixels. An extension of this algorithm for 2D spatial coherence is also examined, which calculates four medians per step.

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