Multiscale Retinex

By Ana Belén Petro, Catalina Sbert, Jean-Michel Morel. Image Processing On Line, 2014

325 citations recorded by the source corpus.

While the retinex theory aimed at explaining human color perception, its derivations have led to efficient algorithms enhancing local image contrast, thus permitting among other features, to “see in the shadows”. Among these derived algorithms, Multiscale Retinex is probably the most successful center-surround image filter. In this paper, we offer an analysis and implementation of Multiscale Retinex. We point out and resolve some ambiguities of the method. In particular, we show that the important color correction final step of the method can be seriously improved. This analysis permits to formulate an automatic implementation of Multiscale Retinex which is as faithful as possible to the one described in the original paper. Overall, this implementation delivers excellent results and confirms the validity of Multiscale Retinex for image color restoration and contrast enhancement. Nevertheless, while the method parameters can be fixed, we show that a crucial choice must be left to the user, depending on the lightning condition of the image: the method must either be applied to each color independently if a color balance is required, or to the luminance only if the goal is to achieve local contrast enhancement. Thus, we propose two slightly different algorithms to deal with both cases.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a65bf739ecfe706e3f3a7c6bf6d56239dfbc080b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Ge Li
2. Yiannis Demiris
3. Zhenqiang Ying
4. Wen Gao
5. Xingchen Zhang

In Computer Vision and Pattern Recognition

1. Ge Li
2. Yiannis Demiris
3. Xiaohui Yuan
4. Wencheng Wang
5. Zairui Gao
6. Zhenqiang Ying
7. Wen Gao
8. Xiaojin Wu

In Materials Chemistry

1. Ge Li
2. Yiannis Demiris
3. Zhenqiang Ying
4. Wen Gao
5. Xingchen Zhang

Citations in the corpus

• An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals (2018, unknown): “The modi fi ed version of the MSRCR f msrcr k ( x , y ) from the research in Petro et al. (2014) can be computed as where α controls the strength of the non-linearity and λ is a constant.”
• A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement (2017, methodology): “…the performance of our method, we compare it with several state-of-the-art methods, including Multi Scale Retinex with Color Restoration (MSRCR) [45], Natural-ness Preserved Enhancement algorithm (NPE) [16], dehazing based method (Dong) [38], Multi-deviation Fusion method (MF) [41],…”
• Visible and Infrared Image Fusion Using Deep Learning (2023, methodology): “They used fused images generated using Laplacian pyramid algo-rithm accompanied by MultiScale Retinex [144] as ground truth.”
• An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “Petro et al. proposed a multiscale Retinex algorithm with chromaticity preservation (MSRCP) [127].”