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Journal of Scientific Computing
Erschienen in: Journal of Scientific Computing 2/2024

01.02.2024

Low-light Image Enhancement via Dual Reflectance Estimation

verfasst von: Fan Jia, Tiange Wang, Tieyong Zeng

Erschienen in: Journal of Scientific Computing | Ausgabe 2/2024

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Abstract

Improving the quality of low-light images is a fundamental task with vast applications in computer vision. Retinex-based methods which decompose the images into reflectance and illumination components have been actively studied over the past years. In this paper, we propose a Retinex-based method with dual reflectance estimation. To be precise, we start with a simple reflectance estimation based on the HSV color space, which is then accompanied by another variational-based estimation of both the reflectance and illumination. Finally, we bring a new perspective to the Retinex model by reconstructing the normal-light image with a novel transformation map given by the estimated reflectance and illumination, which we call radiance mapping. Extensive experiments show that our method obtains outstanding results, both numerically and visually, compared to state-of-the-art methods.
Vorheriger Artikel Recovery Type a Posteriori Error Estimation of an Adaptive Finite Element Method for Cahn–Hilliard Equation
Nächster Artikel Convergence Analysis for Virtual Element Discretizations of the Cardiac Bidomain Model

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Fußnoten
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Metadaten
Titel
Low-light Image Enhancement via Dual Reflectance Estimation
verfasst von
Fan Jia
Tiange Wang
Tieyong Zeng
Publikationsdatum
01.02.2024
Verlag
Springer US
Erschienen in
Journal of Scientific Computing / Ausgabe 2/2024
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI
https://doi.org/10.1007/s10915-023-02431-y

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