Mathematical definition and analysis of the Retinex algorithm

Provenzi, Edoardo; Carli, Luca De; Rizzi, Alessandro; Marini, Daniele

doi:10.1364/josaa.22.002613

Cited by 193 publications

(136 citation statements)

References 22 publications

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“…In order to develop a generic model that can truthfully predict PDR, sensory and cognitive processes involved in extraction of this attribute from complex stimuli need to be taken into account. Previous research has shown that the area of the brightest patches and the image topology affect the perception of lightness and contrast [17,19,26], and therefore should be considered in constructing such a PDR model. Furthermore, image contrast and colorfulness are important factors in visual perception and, based on previous findings [8,11,24,25], could be involved in the process related to the extraction of observed image attributes.…”

Section: Discussionmentioning

confidence: 99%

“…Compared with the anchoring theories, the Retinex theory indirectly arrives to the same conclusion through a probabilistic formulation. This is achieved by averaging luminance values along paths of pixels originating from each point of the picture [19], while taking into account the relative distance between patches of different brightness.…”

Section: Perceived Lightnessmentioning

confidence: 99%

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A model of perceived dynamic range for HDR images

Hulusić

Debattista

Valenzise

et al. 2017

Signal Processing: Image Communication

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For High Dynamic Range (HDR) content, the dynamic range of an image is an important characteristic in algorithm design and validation, analysis of aesthetic attributes and content selection. Traditionally, it has been computed as the ratio between the maximum and minimum pixel luminance, a purely objective measure; however, the human visual system's perception of dynamic range is more complex and has been largely neglected in the literature. In this paper, a new methodology for measuring perceived dynamic range (PDR) of chromatic and achromatic HDR images is proposed. PDR can benefit HDR in a number of ways: for evaluating inverse tone mapping operators and HDR compression methods; aesthetically; or as a parameter for content selection in perceptual studies. A subjective study was conducted on a data set of 36 chromatic and achromatic HDR images. Results showed a strong agreement across participants' allocated scores. In addition, a high correlation between ratings of the chromatic and achromatic stimuli was found. Based on the results from a pilot study, five objective measures (pixel-based dynamic range, image key, area of bright regions, contrast and colorfulness) were selected as candidates for a PDR predictor model; two of which have been found to be significant contributors to the model. Our analyses show that this model performs better than individual metrics for both achromatic and chromatic stimuli.

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Section: Discussionmentioning

confidence: 99%

Section: Perceived Lightnessmentioning

confidence: 99%

A model of perceived dynamic range for HDR images

Hulusić

Debattista

Valenzise

et al. 2017

Signal Processing: Image Communication

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“…This is done on each chromatic channel separately. We briefly recall the mathematical description of retinex given in [35].…”

Section: A MI Retinexmentioning

confidence: 99%

QBRIX: a quantile-based approach to retinex

2014

Self Cite

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In this paper, we introduce a novel probabilistic version of retinex. It is based on a probabilistic formalization of the random spray retinex sampling and contributes to the investigation of the spatial properties of the model. Various versions available of the retinex algorithm are characterized by different procedures for exploring the image content (so as to obtain, for each pixel, a reference white value), then used to rescale the pixel lightness. Here we propose an alternative procedure, which computes the reference white value from the percentile values of the pixel population. We formalize two versions of the algorithm: one with global and one with local behavior, characterized by different computational costs.

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“…On the other hand, Retinex-based methods are based on the human color perception system [10,11]. Provenzi et al mathematically analyzed the Retinex algorithm and demonstrated the performance according to various parameters such as threshold and the number of path of light paths to a pixel [11].…”

Section: Introductionmentioning

confidence: 99%

“…Provenzi et al mathematically analyzed the Retinex algorithm and demonstrated the performance according to various parameters such as threshold and the number of path of light paths to a pixel [11]. The Retinex methods enhance the input image by eliminating the illumination component using the low-pass filtering and logarithmic transformation [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Low-light image restoration using bright channel prior-based variational Retinex model

Park

Moon

et al. 2017

J Image Video Proc.

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This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization. The proposed method first estimates the bright channel to control the amount of brightness enhancement. Next, the variational Retinex-based energy function is iteratively minimized to estimate the improved illumination and reflectance using the BCP. Contrast of the estimated illumination is enhanced using the gamma correction and histogram equalization to reduce a color distortion and noise amplification. Experimental results show that the proposed method can provide the better restored result than the existing methods without unnatural artifacts such as noise amplification and halo effects near edges.

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Mathematical definition and analysis of the Retinex algorithm

Cited by 193 publications

References 22 publications

A model of perceived dynamic range for HDR images

A model of perceived dynamic range for HDR images

QBRIX: a quantile-based approach to retinex

Low-light image restoration using bright channel prior-based variational Retinex model

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