IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Framework for Retinex
International Journal of Computer Vision
What geometric visual hallucinations tell us about the visual cortex
Neural Computation
A new algorithm for unsupervised global and local color correction
Pattern Recognition Letters - Special issue: Colour image processing and analysis
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Perceptually Inspired Variational Framework for Color Enhancement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Random Spray Retinex: A New Retinex Implementation to Investigate the Local Properties of the Model
IEEE Transactions on Image Processing
Perceptual Color Correction Through Variational Techniques
IEEE Transactions on Image Processing
A PDE formalization of retinex theory
IEEE Transactions on Image Processing
Perceptually motivated automatic color contrast enhancement based on color constancy estimation
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
A Total Variation Model for Retinex
SIAM Journal on Imaging Sciences
Trilateral filtering-based retinex for image enhancement
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
A Wavelet Perspective on Variational Perceptually-Inspired Color Enhancement
International Journal of Computer Vision
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We 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 only increase pixel intensities, it is not able to enhance over-exposed pictures. Comparing the analytical structure of KBR with that of a recent variational model of color image enhancement, we are able to perform an analysis of the action of KBR on contrast, showing the need to anti-symmetrize its equation in order to produce a two-sided contrast modification, able to enhance both under and over-exposed pictures. The anti-symmetrized KBR equations show clear correspondences with other existing color correction models, in particular ACE, whose relationship with Retinex has always been difficult to clarify. Finally, from an image processing point of view, we mention that both KBR and its antisymmetric version are free from the chromatic noise due to the use of paths in the original Retinex implementation and that they can be suitably approximated in order to reduce their computational complexity from $\mathcal{O}(N^{2})$ to $\mathcal{O}(N\log N)$ , being N the number of input pixels.