QBRIX: a quantile-based approach to retinex

An Image Contrast Measure Based on Retinex Principles

The image contrast is a feature capturing the variation of the image signal across the space. Such a feature is very useful to describe the local image structure at different scales and thus it is relevant to many computer vision applications, like image/texture retrieval and object recognition. In this work, we present MiRCo, a novel measure of image contrast derived from the Retinex theory. MiRCo is robust against in-plane rotations and light changes at multiple scales. Thanks to these properties, MiRCo enables an accurate and robust description of the local image structure. Here we describe and discuss the mathematical insights of MiRCo also in comparison with other popular contrast measures.

Generalized equation for real-world image enhancement by Milano Retinex family

Milano Retinexes are spatial color algorithms grounded on the Retinex theory and widely applied to enhance the visual content of real-world color images. In this framework, they process the color channels of the input image independently and re-scale channel by channel the intensity of each pixel p by the so-called local reference white, i.e., a strictly positive value, computed by reworking a set of features sampled around p. The neighborhood of p to be sampled, its sampling, the features to be processed, as well as the mathematical model for the computation of the local reference white vary from algorithm to algorithm, determining different levels of enhancement. Based on the analysis of a group of Milano Retinexes, this work proves that the Milano Retinex local reference whites can be expressed by a generalized equation whose parameters model specific aspects of the Milano Retinex spatial color processing. In particular, tuning these parameters leads to different Milano Retinex implementations. This study contributes to a better understanding of the similarities and differences among the members of the Milano Retinex family, and provides new taxonomic schemes of them based on their own mathematical properties.

SuPeR: Milano Retinex implementation exploiting a regular image grid

A spatial color algorithm grounded on the Retinex theory is known as a Milano Retinex. This type of algorithm performs image enhancement by processing spatial and color cues in the neighborhood of each image pixel. Because this local, pixel-wise analysis is time consuming, optimization techniques are needed to expand the use of Milano Retinexes to applications that require fast or even real-time image processing. In this work, we propose SuPeR, an efficient optimization of the Milano Retinex local spatial color processing that exploits superpixels, which are as the regular, rectangular blocks of a grid that partitions the image support. Image enhancement is obtained by reworking channel-wise the intensity of each pixel based on the maximum color intensities of the blocks and on its distance from the blocks. The experiments, carried out on real-world image datasets, show good performance compared to other Milano Retinexes.

STAR: A Segmentation-Based Approximation of Point-Based Sampling Milano Retinex for Color Image Enhancement

Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions.

GRASS: A Gradient-Based Random Sampling Scheme for Milano Retinex

Retinex is an early and famous theory attempting to estimate the human color sensation derived from an observed scene. When applied to a digital image, the original implementation of retinex estimates the color sensation by modifying the pixels channel intensities with respect to a local reference white, selected from a set of random paths. The spatial search of the local reference white influences the final estimation. The recent algorithm energy-driven termite retinex (ETR), as well as its predecessor termite retinex, has introduced a new path-based image aware sampling scheme, where the paths depend on local visual properties of the input image. Precisely, the ETR paths transit over pixels with high gradient magnitude that have been proved to be important for the formation of color sensation. Such a sampling method enables the visit of image portions effectively relevant to the estimation of the color sensation, while it reduces the analysis of pixels with less essential and/or redundant data, i.e., the flat image regions. While the ETR sampling scheme is very efficacious in detecting image pixels salient for the color sensation, its computational complexity can be a limit. In this paper, we present a novel Gradient-based RAndom Sampling Scheme that inherits from ETR the image aware sampling principles, but has a lower computational complexity, while similar performance. Moreover, the new sampling scheme can be interpreted both as a path-based scanning and a 2D sampling.

A population-based approach to point-sampling spatial color algorithms

Inspired by the behavior of the human visual system, spatial color algorithms perform image enhancement by correcting the pixel channel lightness based on the spatial distribution of the intensities in the surrounding area. The two visual contrast enhancement algorithms RSR and STRESS belong to this family of models: they rescale the input based on local reference values, which are determined by exploring the image by means of random point samples, called sprays. Due to the use of sampling, they may yield a noisy output. In this paper, we introduce a probabilistic formulation of the two models: our algorithms (RSR-P and STRESS-P) rely implicitly on the whole population of possible sprays. For processing larger images, we also provide two approximated algorithms that exploit a suitable target-dependent space quantization. Those spray population-based formulations outperform RSR and STRESS in terms of the processing time required for the production of noiseless outputs. We argue that this population-based approach, which can be extended to other members of the family, complements the sampling-based approach, in that it offers not only a better control in the design of approximated algorithms, but also additional insight into individual models and their relationships. We illustrate the latter point by providing a model of halo artifact formation.

Energy-driven path search for Termite Retinex

The human color sensation depends on the local and global spatial arrangements of the colors in the scene. Emulating this dependence requires the exploration of the image in search of a white reference. The algorithm Termite Retinex explores the image by a set of paths resembling traces of a swarm of termites. Starting from this approach, we develop a novel spatial exploration scheme where the termite paths are local minimums of an energy function, which depend on the image visual content. The energy is designed to favor the visitation of regions containing information relevant to the color sensation while minimizing the coverage of less essential regions. This exploration method contributes to the investigation of the spatial properties of the color sensation and, to the best of our knowledge, is the first model relying on mathematical global conditions for the Retinex paths. The experiments show that the estimation of the color sensation obtained by means of the proposed spatial sampling is a valid alternative to the one based on Termite Retinex.