Retinex in MATLAB™

Funt, Brian V.; Ciurea, Florian; McCann, John J.

doi:10.1117/1.1636761

Cited by 303 publications

(206 citation statements)

References 24 publications

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“…To form the output image, the matrix of adimensional values, spatially recomputed in the first phase, is scaled in the second phase onto the available output dynamic range, according to global anchoring principles and to the model goal. In this second phase, LUT and/or gamma non-linear adjustment can be added according to the SCA goal [7]. Due to this characteristic, SCAs can be suitable for High Dynamic Range (HDR) tone rendering, and/or for conversion of different dynamic range derived from changes of support, like e.g.…”

Section: Spatial Color Algorithms (Scas)mentioning

confidence: 99%

Unsupervised digital movie restoration with spatial models of color

Rizzi

Berolo

Bonanomi

et al. 2014

Multimed Tools Appl

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In this paper, we present an efficient approach to digital color restoration, based on the idea of recovering the appearance of color rather than the original color signal, since, in most of the cases, for old films the original reference is missing, and new films or digital coding can be subject to severe gamut transformations. This approach is based on the application of algorithms inspired by the capabilities of the Human Vision System of automatically adjust to the variation of color and lightness in the scene. The proposed method allows more unsupervised restoration. We present an overview of the approach, characteristics of this family of algorithms, and restoration examples.

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Section: Spatial Color Algorithms (Scas)mentioning

confidence: 99%

Unsupervised digital movie restoration with spatial models of color

Rizzi

Berolo

Bonanomi

et al. 2014

Multimed Tools Appl

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“…Perhaps the most well known color constancy algorithm is Retinex [1], which attempts to mimic the human sensory response in psychophysics experiments. Retinex is based on the assumption that a given pixel's lightness depends on its own reflectance and the lightness of neighboring pixels.…”

Section: Retinexmentioning

confidence: 99%

“…The majority of algorithms, such as Retinex [1], have been designed primarily to enhance photographs taken under various illuminants. Their ability to improve object and face recognition has been infrequently investigated, despite the fact that this is a very important trait for an image recognition system to have.…”

Section: Introductionmentioning

confidence: 99%

Color Constancy Algorithms for Object and Face Recognition

Kanan

Flores

Cottrell

2010

Advances in Visual Computing

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Abstract. Brightness and color constancy is a fundamental problem faced in computer vision and by our own visual system. We easily recognize objects despite changes in illumination, but without a mechanism to cope with this, many object and face recognition systems perform poorly. In this paper we compare approaches in computer vision and computational neuroscience for inducing brightness and color constancy based on their ability to improve recognition. We analyze the relative performance of the algorithms on the AR face and ALOI datasets using both a SIFT-based recognition system and a simple pixel-based approach. Quantitative results demonstrate that color constancy methods can significantly improve classification accuracy. We also evaluate the approaches on the Caltech-101 dataset to determine how these algorithms affect performance under relatively normal illumination conditions.

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“…An overview is out of the scope of this paper; however, these implementations can be divided into two major groups and differ in the ways they achieve locality. The first group, among which we can mention RSR, [1,9,11,[19][20][21] uses a sampling approach: the neighborhood of each pixel is explored either using paths or extracting random pixels; the second group [23,[29][30][31][32] computes values over the image with convolution masks or weighting distances. An extensive review on retinex, including recent PDE and variational implementations, can be found in [33].…”

Section: Image Sampling In Retinex and Rsrmentioning

confidence: 99%

“…Among the many image sampling methods used, we recall predefined [19][20][21], constrained [22], or Brownian random paths (isotropic memoryless random walks) [11], fixed masks [23], random points [24], multilevel image decomposition [20,21,25]. Moreover, several variational formulations of the model also have been developed so far [6,10,12,26,27].…”

Section: Introductionmentioning

confidence: 99%

QBRIX: a quantile-based approach to retinex

2014

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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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Retinex in MATLAB™

Cited by 303 publications

References 24 publications

Unsupervised digital movie restoration with spatial models of color

Unsupervised digital movie restoration with spatial models of color

Color Constancy Algorithms for Object and Face Recognition

QBRIX: a quantile-based approach to retinex

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