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María Herrera-Arellano

dblp:293/3831 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0001-8893-7882ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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 · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image enhancement
contrast enhancement
0.512021
Visible-NIR Image Fusion Based on Top-Hat Transform · IEEE Trans. Image Process. 2021
Image and video processing
image enhancement
0.512021
Visible-NIR Image Fusion Based on Top-Hat Transform · IEEE Trans. Image Process. 2021
Image and video processing
image fusion
0.512021
Visible-NIR Image Fusion Based on Top-Hat Transform · IEEE Trans. Image Process. 2021

Methods — techniques the papers use, named apart from their topics

top-hat transform · 0.5
YearPublicationVenuePosition
2021 Visible-NIR Image Fusion Based on Top-Hat Transform
abstract
The near-infrared band of the electromagnetic spectrum has become an important tool for enhancing image quality. Commonly, outdoor color images are degraded by bad weather conditions that lead to a loss of contrast and fine details in color images since light scattering produces attenuation and smoothing effects. Despite the fact that current Visible-NIR fusion methods achieve image enhancement features, some issues like edge preservation and color oversaturation still need to be addressed. In this work, a method that performs a selective Visible-NIR fusion of the most relevant image structures through top-hat transform is proposed. The performance of the method is evaluated by quantifying the new information added to the image and the change in color. Experimental results show a high degree of detail in preserving the edges while maintaining the color of the image. Moreover, the proposed method demonstrates that the image quality improvements were not significantly affected by a change in the color space.
María Herrera-Arellano, Hayde Peregrina-Barreto, Iván R. Terol-Villalobos
IEEE Trans. Image Process.1