Issues About Retinex Theory and Contrast Enhancement

@article{Bertalmo2009IssuesAR,
  title={Issues About Retinex Theory and Contrast Enhancement},
  author={Marcelo Bertalm{\'i}o and Vicent Caselles and Edoardo Provenzi},
  journal={International Journal of Computer Vision},
  year={2009},
  volume={83},
  pages={101-119}
}
AbstractWe present an interpretation of Land’s Retinex theory that we show to be consistent with the original formulation. The proposed model relies on the computation of the expectation value of a suitable random variable weighted with a kernel function, thus the name Kernel-Based Retinex (KBR) for the corresponding algorithm. KBR shares the same intrinsic characteristics of the original Retinex: it can reduce the effect of a color cast and enhance details in low-key images but, since it can… 
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169 Citations
Highly Influential Citations
6
Background Citations
53
Methods Citations
46
Results Citations
2

169 Citations

Retinex based on exponent-type total variation scheme

A novel variational model to effectively and robustly restore images degenerated by both illusion and noises is proposed by incorporating appropriate regularization technique for the reflectance component and illumination component accordingly.

Efficient image enhancement using sparse source separation in the Retinex theory

This paper applies the sparse source separation technique to the Retinex theory to present a physics-based algorithm that satisfies the locality characteristic of the original RetineX theory and can very nearly replicate the original image without any special operation.

On the Duality Between Retinex and Image Dehazing

Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.

A PDE Formalization of Retinex Theory

It is proved that if the paths are assumed to be symmetric random walks, the Retinex solutions satisfy a discrete Poisson equation and this formalization yields an exact and fast implementation using only two FFTs.

Achromatic Induction: A Variational Interpretation of Rudd-Zemach's Edge Integration Model

  • E. Provenzi
  • Computer Science
    2013 International Conference on Signal-Image Technology & Internet-Based Systems
  • 2013
Rudd-Zemach's edge integration model is examined and it is shown that it can be interpreted in terms of a variational framework that shares many features with a phenomenologically-based visual perception theory recently proposed, opening the possibility to determine important parameters of this framework.

A unifying retinex model based on non-local differential operators

Within a single framework new retinex instances particularly suited for texture-preserving shadow removal, cartoon-texture decomposition, color and hyperspectral image enhancement are defined, and entirely novel retineX formulations are derived by using more interesting non-local versions for the sparsity and fidelity prior.

Non-Local Retinex - A Unifying Framework and Beyond

This paper reinterpret the gradient thresholding model as variational models with sparsity constraints as well as defining the unifying Retinex model in two similar, but more general, steps.

A Local Flatness Based Variational Approach to Retinex

The experimental results show that the new approach to digitally implementing the Retinex using a local deviation based variational model can reconstruct more accurate recovered images than other state-of-the-art methods, while maintaining good contrast.

A parallel operator splitting algorithm for solving constrained total-variation retinex

A parallel operator splitting algorithm tailored for the constrained total-variation retinex model, in which all the resulting subproblems admit closed form solutions or can be tractably solved by some subroutines without any internally nested iterations is developed.

A Retinex model based on Absorbing Markov Chains

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