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Logarithmic Imaging and Sensing
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Logarithmic Imaging and Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4423

Special Issue Editor

Prof. Dr. Michel Jourlin

E-Mail Website
Guest Editor
Hubert Curien Laboratory, Saint-Etienne University, 42023 Saint-Etienne, France
Interests: Image Processing; Logarithmic Image Processing; Biomedical and Industrial Image Processing

Special Issue Information

Dear Colleagues,

The performance of image sensors highly depends on illumination conditions. As an alternative, image sensors with a logarithmic response are capable of acquiring illumination-invariant images. Planty of theoretical and applied papers have been published around the logarithmic image processing (LIP) model from its creation until today, proving its efficiency in particular for images acquired under uncontrolled and/or very low lighting. Such a model deals with grey level as well as color images. Other different yet similar models have been proposed such as the SLIP (symmetric LIP), the GLIP (generalized LIP), the PLIP (parametrization of LIP), etc., offering new concepts and various applications.

The Special Issue aims at focusing on state-of-the-art research in the domain of logarithmic imaging and sensing, including new developments currently arising linked with artificial intelligence and deep learning, with mathematical morphology or with other existing theories and successfully applied in various fields (biomedical, industry, safety, military, etc.). 

Prof. Dr. Michel Jourlin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • logarithmic image processing
  • sensing
  • low lighting
  • deep learning
  • artificial intelligence
  • mathematical morphology

Published Papers (2 papers)

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Research

11 pages, 5383 KiB  
Article
Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising
by Maxime Carré and Michel Jourlin
Sensors 2021, 21(23), 7906; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237906 - 27 Nov 2021
Cited by 1 | Viewed by 1152
Abstract
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality [...] Read more.
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately. Full article
(This article belongs to the Special Issue Logarithmic Imaging and Sensing)
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21 pages, 1573 KiB  
Article
A Parametric Logarithmic Image Processing Framework Based on Fuzzy Graylevel Accumulation by the Hamacher T-Conorm
by Constantin Vertan, Corneliu Florea and Laura Florea
Sensors 2021, 21(14), 4857; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144857 - 16 Jul 2021
Viewed by 2564
Abstract
It has been proven that Logarithmic Image Processing (LIP) models provide a suitable framework for visualizing and enhancing digital images acquired by various sources. The most visible (although simplified) result of using such a model is that LIP allows the computation of graylevel [...] Read more.
It has been proven that Logarithmic Image Processing (LIP) models provide a suitable framework for visualizing and enhancing digital images acquired by various sources. The most visible (although simplified) result of using such a model is that LIP allows the computation of graylevel addition, subtraction and multiplication with scalars within a fixed graylevel range without the use of clipping. It is claimed that a generalized LIP framework (i.e., a parameterized family of LIP models) can be constructed on the basis of the fuzzy modelling of gray level addition as an accumulation process described by the Hamacher conorm. All the existing LIP and LIP-like models are obtained as particular cases of the proposed framework in the range corresponding to real-world digital images. Full article
(This article belongs to the Special Issue Logarithmic Imaging and Sensing)
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