Demo Data from EDBT 2026 / reviewers in the wild / expert
M. Sajjad Abdullah
dblp:30/4344
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2002
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Image and video processing · 77% Computational photography and imaging · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Image and video processing
image estimation |
0.0 | 1 | 2002 | Maximum-likelihood image estimation using photon-correlated beams · IEEE Trans. Image Process. 2002 |
Computational photography and imaging › low-light imaging
photon-limited imaging |
0.0 | 1 | 2002 | Maximum-likelihood image estimation using photon-correlated beams · IEEE Trans. Image Process. 2002 |
Methods — techniques the papers use, named apart from their topics
stochastic modeling · 0.0maximum likelihood estimation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | Maximum-likelihood image estimation using photon-correlated beamsabstractA theory is presented addressing the fundamental limits of image estimation in a setup that uses two photon-correlated beams. These beams have the property that their photon arrivals, as a point process, are ideally synchronized in time and space. The true image represents the spatial distribution of the optical transmittance (or reflectance) of an object. In this setup, one beam is used to probe the image while the other is used as a reference providing additional information on the actual number of photons impinging on the object. This additional information is exploited to reduce the effect of quantum noise associated with the uncertainty in the number of photons per pixel. A stochastic model for the joint statistics of the two observation matrices is developed and used to obtain a local maximum-likelihood estimator of the image. The model captures the nonideal nature of the correlation between the photons of the beams by means of a simple random translation model. The mean-square error of the estimator is evaluated and compared to the corresponding conventional techniques. Conditions for the performance advantage of the proposed estimator are examined in terms of key system parameters. The theoretical predictions are demonstrated by means of simulation. Majeed M. Hayat, M. Sajjad Abdullah, Adel Joobeur, Bahaa E. A. Saleh |
IEEE Trans. Image Process. | 2 |