NDLI: Point-based spatial colour sampling in Milano-Retinex: a survey

Content Provider	IET Digital Library
Author	Lecca, Michela Simone, Gabriele Bonanomi, Cristian Rizzi, Alessandro
Abstract	Milano-Retinex is a family of Retinex-inspired spatial colour algorithms mainly developed for colour image enhancement. According to the Retinex theory, a Milano-Retinex algorithm takes as input an RGB image and processes the colour intensities of each pixel (i.e. the target) based on the spatial distribution of the colour intensities sampled in a surrounding region. The output is an RGB image, with locally adjusted colours and contrast. In Milano-Retinex family, different ways of spatial sampling are implemented. This study reviews and compares these sampling characteristics within a group of Milano-Retinex algorithms developed in the last decade, from Random Spray Retinex (2007) to the gradient-based colour sampling schemes GREAT and GRASS (2017). Instead of exploring the target neighbourhood by random paths as the original Retinex algorithm does, these methods consider sets of pixels, randomly or deterministically defined, including all the image pixels or a part of them, such as random sprays or image edges. They replace the ratio-reset-threshold-product-average mechanism of the original Retinex with equations re-working maximal intensities over the sampled sets. The performance of these approaches is compared with more than 200 images of indoor and outdoor scenes, captured by commercial cameras under several different conditions.
Starting Page	833
Ending Page	849
Page Count	17
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.1224
Journal	IET Image Processing
Publisher Date	2018-02-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Colour Appearance Estimation Colour Image Enhancement Colour Intensity Spatial Distribution Combinatorial Mathematics Computer Vision And Image Processing Technique Gradient Method Gradient-based Colour Sampling Scheme Gradient-based Random Sampling Scheme GRASS GREAT Image Colour Analysis Image Enhancement Image Sampling Interpolation And Function Approximation Milano-Retinex Algorithm Numerical Analysis Optical, Image And Video Signal Processing Point-based Spatial Colour Sampling Random Spray Retinex Retinex Theory Retinex-inspired Spatial Colour Algorithm RGB Image Set Theory
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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