Variational low-light image enhancement based on fractional-order differential
DOI10.4208/cicp.oa-2022-0197zbMATH Open1539.49007MaRDI QIDQ6537071
Yang Wang, Tieyong Zeng, Qianting Ma
Publication date: 14 May 2024
Published in: Communications in Computational Physics (Search for Journal in Brave)
Computer science aspects of computer-aided design (68U07) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Existence theories for optimal control problems involving relations other than differential equations (49J21)
Cites Work
- Title not available (Why is that?)
- A fractional-order derivative based variational framework for image denoising
- Global convergence of ADMM in nonconvex nonsmooth optimization
- A variational framework for retinex
- A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography
- Single image blind deblurring based on the fractional-order differential
- A variational model with hybrid hyper-Laplacian priors for Retinex
- A detail preserving variational model for image Retinex
- The Split Bregman Method for L1-Regularized Problems
- Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
- LIME: Low-Light Image Enhancement via Illumination Map Estimation
- A Novel Retinex-Based Fractional-Order Variational Model for Images With Severely Low Light
- A Local Flatness Based Variational Approach to Retinex
- A Proximal Point Dual Newton Algorithm for Solving Group Graphical Lasso Problems
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