A new histogram equalization technique for contrast enhancement of grayscale images using the differential evolution algorithm

Rivera-Aguilar, Beatriz A.; Cuevas, Erik; Pérez, Marco; Camarena, Octavio; Rodríguez, Alma

doi:10.1007/s00521-024-09739-2

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-09739-2

|View full text |Cite

A new histogram equalization technique for contrast enhancement of grayscale images using the differential evolution algorithm

Beatriz A. Rivera-Aguilar,

Erik Cuevas,

Marco Pérez

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Research on Target Image Classification in Low-Light Night Vision

Li,

Luo,

Zheng

et al. 2024

Entropy

View full text Add to dashboard Cite

In extremely dark conditions, low-light imaging may offer spectators a rich visual experience, which is important for both military and civic applications. However, the images taken in ultra-micro light environments usually have inherent defects such as extremely low brightness and contrast, a high noise level, and serious loss of scene details and colors, which leads to great challenges in the research of low-light image and object detection and classification. The low-light night vision image used as the study object in this work has an excessively dim overall picture and very little information about the screen’s features. Three algorithms, HE, AHE, and CLAHE, were used to enhance and highlight the image. The effectiveness of these image enhancement methods is evaluated using metrics such as the peak signal-to-noise ratio and mean square error, and CLAHE was selected after comparison. The target image includes vehicles, people, license plates, and objects. The gray-level co-occurrence matrix (GLCM) was used to extract the texture features of the enhanced images, and the extracted image texture features were used as input to construct a backpropagation (BP) neural network classification model. Then, low-light image classification models were developed based on VGG16 and ResNet50 convolutional neural networks combined with low-light image enhancement algorithms. The experimental results show that the overall classification accuracy of the VGG16 convolutional neural network model is 92.1%. Compared with the BP and ResNet50 neural network models, the classification accuracy was increased by 4.5% and 2.3%, respectively, demonstrating its effectiveness in classifying low-light night vision targets.

show abstract

Research on Target Image Classification in Low-Light Night Vision

Li,

Luo,

Zheng

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A new histogram equalization technique for contrast enhancement of grayscale images using the differential evolution algorithm

Cited by 1 publication

References 48 publications

Research on Target Image Classification in Low-Light Night Vision

Research on Target Image Classification in Low-Light Night Vision

Contact Info

Product

Resources

About