Histogram Equalization Algorithm Based on Guided Stratification Infrared Image Enhancement Algorithm

Infrared images have the disadvantages of low contrast and being greatly affected by the environment. To address these issues, the article proposes an image enhancement algorithm based on an improved layered dual-histogram equalization method. The image first undergoes bilateral filtering for denoising. The denoised image is then processed using guided filtering for layering, dividing it into a detail layer and a background layer. For the background layer, an improved dual-histogram equalization method is applied to enhance the overall contrast. For the detail layer, bilateral processing is used to suppress noise. Then, introduce secondary noise suppression based on the mean of non-zero pixel value as a threshold. Gamma mapping is applied to the detail layer. Finally, the processed background and detail layers are fused. The algorithm effectively suppresses noise in infrared images and enhances contrast, with a 2.1 improvement in the contrast index.

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Published
2024-12-27
DOI
https://doi.org/10.1145/3722405.3722420
Primary Topic
Image Enhancement Techniques
Type
article
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article

Histogram Equalization Algorithm Based on Guided Stratification Infrared Image Enhancement Algorithm

Wei Song, Keqing Ning, Guanyuan Li, Na Xing et al.
Image Enhancement Techniques
article

Histogram Equalization Algorithm Based on Guided Stratification Infrared Image Enhancement Algorithm

Wei Song, Keqing Ning, Guanyuan Li, Na Xing, Haonan Gao
article en

Abstract

Infrared images have the disadvantages of low contrast and being greatly affected by the environment. To address these issues, the article proposes an image enhancement algorithm based on an improved layered dual-histogram equalization method. The image first undergoes bilateral filtering for denoising. The denoised image is then processed using guided filtering for layering, dividing it into a detail layer and a background layer. For the background layer, an improved dual-histogram equalization method is applied to enhance the overall contrast. For the detail layer, bilateral processing is used to suppress noise. Then, introduce secondary noise suppression based on the mean of non-zero pixel value as a threshold. Gamma mapping is applied to the detail layer. Finally, the processed background and detail layers are fused. The algorithm effectively suppresses noise in infrared images and enhances contrast, with a 2.1 improvement in the contrast index.

North China University of Technology (CN), Jiangsu Institute of Metrology (CN)
Openalex Percentile: Top 70%
Image Enhancement Techniques
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Histogram Equalization Algorithm Based on Guided Stratification Infrared Image Enhancement Algorithm — Wei Song, Keqing Ning, et al. · (2024) | TGRS Research Map | TGRS