Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram. / Medina Caballero, Rubén Darío; Brizuela Pineda, Isidro Augusto; Mello Román, Julio César; Vázquez Noguera, José Luis; Caceres Silva, Juan.

In: Infrared Physics & Technology, Vol. 99, 06.2019, p. 257-264.

Research output: Contribution to journalArticlepeer-review

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Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram. / Medina Caballero, Rubén Darío; Brizuela Pineda, Isidro Augusto; Mello Román, Julio César; Vázquez Noguera, José Luis; Caceres Silva, Juan.

In: Infrared Physics & Technology, Vol. 99, 06.2019, p. 257-264.

Research output: Contribution to journalArticlepeer-review

Harvard

Medina Caballero, RD, Brizuela Pineda, IA, Mello Román, JC, Vázquez Noguera, JL & Caceres Silva, J 2019, 'Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram', Infrared Physics & Technology, vol. 99, pp. 257-264. https://doi.org/10.1016/j.infrared.2019.03.016

APA

Medina Caballero, R. D., Brizuela Pineda, I. A., Mello Román, J. C., Vázquez Noguera, J. L., & Caceres Silva, J. (2019). Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram. Infrared Physics & Technology, 99, 257-264. https://doi.org/10.1016/j.infrared.2019.03.016

Vancouver

Medina Caballero RD, Brizuela Pineda IA, Mello Román JC, Vázquez Noguera JL, Caceres Silva J. Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram. Infrared Physics & Technology. 2019 Jun;99:257-264. https://doi.org/10.1016/j.infrared.2019.03.016

Author

Medina Caballero, Rubén Darío ; Brizuela Pineda, Isidro Augusto ; Mello Román, Julio César ; Vázquez Noguera, José Luis ; Caceres Silva, Juan. / Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram. In: Infrared Physics & Technology. 2019 ; Vol. 99. pp. 257-264.

BibTeX

@article{19d88b3bf6094cee802ca42d8454c055,
title = "Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram",
abstract = "In infrared images, the pixels representing the objects are hidden in a large number of background pixels with low contrast. Several effective contrast enhancement techniques exist in the state of the art today, however they cause the noise level added to the images to increase. The improvement of contrast is an indispensable procedure for the analysis of infrared images, due to the scarce temperature difference between the objects and the background, captured by the surveillance systems using infrared sensors. Therefore, a contrast enhancement algorithm for infrared imaging based on histogram equalization using clipping is presented in this article. The proposed algorithm divides the histogram into 4 subhistograms, then each subhistogram is modified with a cutlimit based on the size of the subhistogram in order to limit the improvement of the contrast. The experimental results prove that the algorithm improves the contrast of infrared images by 99%, especially the contrast between the objects and the background of the infrared images preserving the mean brightness and decreasing the aggregate noise level of them. With the proposed algorithm,the background of the infrared image is restricted while the objects are visually contrasted.",
keywords = "Contrast enhancement, Clipping, Histogram Equalization, Mean brightness",
author = "{Medina Caballero}, {Rub{\'e}n Dar{\'i}o} and {Brizuela Pineda}, {Isidro Augusto} and {Mello Rom{\'a}n}, {Julio C{\'e}sar} and {V{\'a}zquez Noguera}, {Jos{\'e} Luis} and {Caceres Silva}, Juan",
year = "2019",
month = jun,
doi = "10.1016/j.infrared.2019.03.016",
language = "English",
volume = "99",
pages = "257--264",
journal = "Infrared Physics & Technology",
issn = "1350-4495",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram

AU - Medina Caballero, Rubén Darío

AU - Brizuela Pineda, Isidro Augusto

AU - Mello Román, Julio César

AU - Vázquez Noguera, José Luis

AU - Caceres Silva, Juan

PY - 2019/6

Y1 - 2019/6

N2 - In infrared images, the pixels representing the objects are hidden in a large number of background pixels with low contrast. Several effective contrast enhancement techniques exist in the state of the art today, however they cause the noise level added to the images to increase. The improvement of contrast is an indispensable procedure for the analysis of infrared images, due to the scarce temperature difference between the objects and the background, captured by the surveillance systems using infrared sensors. Therefore, a contrast enhancement algorithm for infrared imaging based on histogram equalization using clipping is presented in this article. The proposed algorithm divides the histogram into 4 subhistograms, then each subhistogram is modified with a cutlimit based on the size of the subhistogram in order to limit the improvement of the contrast. The experimental results prove that the algorithm improves the contrast of infrared images by 99%, especially the contrast between the objects and the background of the infrared images preserving the mean brightness and decreasing the aggregate noise level of them. With the proposed algorithm,the background of the infrared image is restricted while the objects are visually contrasted.

AB - In infrared images, the pixels representing the objects are hidden in a large number of background pixels with low contrast. Several effective contrast enhancement techniques exist in the state of the art today, however they cause the noise level added to the images to increase. The improvement of contrast is an indispensable procedure for the analysis of infrared images, due to the scarce temperature difference between the objects and the background, captured by the surveillance systems using infrared sensors. Therefore, a contrast enhancement algorithm for infrared imaging based on histogram equalization using clipping is presented in this article. The proposed algorithm divides the histogram into 4 subhistograms, then each subhistogram is modified with a cutlimit based on the size of the subhistogram in order to limit the improvement of the contrast. The experimental results prove that the algorithm improves the contrast of infrared images by 99%, especially the contrast between the objects and the background of the infrared images preserving the mean brightness and decreasing the aggregate noise level of them. With the proposed algorithm,the background of the infrared image is restricted while the objects are visually contrasted.

KW - Contrast enhancement

KW - Clipping

KW - Histogram Equalization

KW - Mean brightness

U2 - 10.1016/j.infrared.2019.03.016

DO - 10.1016/j.infrared.2019.03.016

M3 - Article

VL - 99

SP - 257

EP - 264

JO - Infrared Physics & Technology

JF - Infrared Physics & Technology

SN - 1350-4495

ER -