Low-light image enhancement via dual reflectance estimation

DOI10.1007/S10915-023-02431-YOpenAlexW4390541847MaRDI QIDQ6178649

Fan Jia, Tieyong Zeng, Tiange Wang

Publication date: 16 January 2024

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-023-02431-y

zbMATH Keywords

variational methodsMumford-Shah modelretinexlow-light image enhancement

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

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• A Total Variation Model for Retinex
• The Split Bregman Method for L1-Regularized Problems
• A Variational Model with Barrier Functionals for Retinex
• Perceptual Quality Assessment for Multi-Exposure Image Fusion
• Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
• LIME: Low-Light Image Enhancement via Illumination Map Estimation
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model
• Lightening Network for Low-Light Image Enhancement
• Non-Local Retinex---A Unifying Framework and Beyond
• A Nonlocal Total Variation Model for Image Decomposition: Illumination and Reflectance
• Scale Space and PDE Methods in Computer Vision

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