Evidence map›Paper›PMID 39124018›Full record

ArticleSensors (Basel, Switzerland)2024

Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework.

Michel Jourlin

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Michel JourlinLaboratoire Hubert Curien, UMR CNRS 5516, 18 Rue Professeur Benoît Lauras, 42000 Saint-Étienne, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study deals with image enhancement, which is a very common problem in image processing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in "real-time", which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.

Indexed as

enhancementfull dynamic rangeimagelogarithmic image processingnegative grey level

Identifiers

PMID39124018
PMCPMC11314874

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.