An Enhanced Histogram Matching Approach Using the Retinal Filter’s Compression Function for Illumination Normalization in Face Recognition

Salaheldin, Ahmed; Nagaty, Khaled Ahmed; Elarif, Taha

doi:10.1007/978-3-540-69812-8_87

Cited by 9 publications

(1 citation statement)

References 13 publications

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“…It reduces noise amplification and over-enhancement. Salah et al [18] proposed a combination of gamma correction and the retinal filter (gamma-HM-COMP), which preserves the contrast between the gray levels of the original pixels, thereby preserving more edge information. Tan et al [19] proposed a background-brightness-preserving HE (BBPHE) based on nonlinear histogram equalization.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

et al. 2023

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Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced illumination information is extracted from the low-frequency component in the HE-enhanced image, and the enhanced edge information is obtained from the high-frequency component in the MSR-enhanced image. By designing adaptive fusion weights of HE and MSR, the proposed method effectively combines enhanced illumination and edge information. The experimental results show that HE-MSR-COM improves the image quality by 23.95% and 10.6% in two datasets, respectively, compared with HE, contrast-limited adaptive histogram equalization (CLAHE), MSR, and gamma correction (GC).

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Section: Histogram Equalizationmentioning

confidence: 99%