QBRIX: a quantile-based approach to retinex

Gianini, G.; Manenti, Andrea; Rizzi, Alessandro

doi:10.1364/josaa.31.002663

Cited by 37 publications

(25 citation statements)

References 29 publications

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“…Due to this characteristic, GRASS can be also viewed as evolution of the 2D spatial sampling proposed by Random Spray Retinex (RSR) [17], where the figure sampling of any pixel x is a set of pixels distributed around x with radial density. However, we highlight that GRASS strongly differs from RSR and from its further versions STRESS [18] and QBRIX [19], because these methods do not take into account the image content for sampling.…”

Section: Introductionmentioning

confidence: 94%

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

Lecca

Rizzi

Serapioni

2017

IEEE Trans. on Image Process.

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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.

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Section: Introductionmentioning

confidence: 94%

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

Lecca

Rizzi

Serapioni

2017

IEEE Trans. on Image Process.

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“…Many SCAs exist, derived from Retinex theory by Land and McCann 15 and Land 16 . Among the many SCA algorithms we recall in particular RSR by Provenzi et al 17 , STRESS by Kolås et al 18 , ACE by Rizzi et al 19 , RACE by Provenzi et al 20 , Termite Retinex by Simone et al 21 and QBRIX by Gianini et al 22 .…”

Section: Spatial Color Algorithms and Clahementioning

confidence: 99%

A novel approach to visual rendering of astro-photographs

2016

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When we perform a visual analysis of a cosmic object photograph the contrast plays a fundamental role. A linear distribution of the observable values is not necessarily the best possible for the Human Visual System (HVS). In fact HVS has a non-linear response, and exploits contrast locally with different stretching for different lightness areas. As a consequence, according to the observation task, local contrast can be adjusted to make easier the detection of relevant information. The proposed approach is based on Spatial Color Algorithms (SCA) that mimic the HVS behavior. These algorithms compute each pixel value by a spatial comparison with all (or a subset of) the other pixels of the image. The comparison can be implemented as a weighted difference or as a ratio-product over given sampling in the neighbor region. A final mapping allows exploiting all the available dynamic range. In the case of color images SCA process separately the three chromatic channels producing an effect of color normalization, without introducing channel cross correlation. We will present very promising results on amateur photographs of deep sky objects. The results are presented for a qualitative and subjective visual evaluation and for a quantitative evaluation through image quality measures, in particular to quantify the effect of algorithms on the noise. Moreover our results help to better characterize contrast measures.

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“…The length of the paths and diameter of the sprays can significantly influence the results, and the optimal settings of these crucial parameters vary among different images. Recently, to reduce the white noises induced by RSR, Gianini et al 6 proposed a quantile-based implementation of retinex, where a weighted version of histogram was built at every target pixel and a quantile was determined to be a referenced white point. However, small quantiles caused the result image to be whitish, while large quantile produces inadequate enhancement for dark areas.…”

Section: Introductionmentioning

confidence: 99%

Adaptive enhancement for nonuniform illumination images via nonlinear mapping

Wang

Huang

2017

J. Electron. Imaging

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Nonuniform illumination images suffer from degenerated details because of underexposure, overexposure, or a combination of both. To improve the visual quality of color images, underexposure regions should be lightened, whereas overexposure areas need to be dimmed properly. However, discriminating between underexposure and overexposure is troublesome. Compared with traditional methods that produce a fixed demarcation value throughout an image, the proposed demarcation changes as local luminance varies, thus is suitable for manipulating complicated illumination. Based on this locally adaptive demarcation, a nonlinear modification is applied to image luminance. Further, with the modified luminance, we propose a nonlinear process to reconstruct a luminance-enhanced color image. For every pixel, this nonlinear process takes the luminance change and the original chromaticity into account, thus trying to avoid exaggerated colors at dark areas and depressed colors at highly bright regions. Finally, to improve image contrast, a local and image-dependent exponential technique is designed and applied to the RGB channels of the obtained color image. Experimental results demonstrate that our method produces good contrast and vivid color for both nonuniform illumination images and images with normal illumination. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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QBRIX: a quantile-based approach to retinex

Cited by 37 publications

References 29 publications

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

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

A novel approach to visual rendering of astro-photographs

Adaptive enhancement for nonuniform illumination images via nonlinear mapping

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