Hue-preserving perceptual contrast enhancement

Pierre, Fabien; Aujol, Jean-François; Bugeau, Aurélie; Steidl, Gabriele; Ta, Vinh-Thong

doi:10.1109/icip.2016.7533124

Cited by 9 publications

(9 citation statements)

References 19 publications

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“…Menotti et al proposed two fast hue-preserving HE methods based on 1D and 2D histograms of RGB color space for color image contrast enhancement [13]. Pierre et al [14] introduced a variational model for the enhancement of color images, and compared their method with the state-of-the-art methods including Nikolova and Steidl's method [15], which is based on their strict ordering algorithm for exact HS [16].…”

Section: Introductionmentioning

confidence: 99%

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

2017

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A large number of color image enhancement methods are based on the methods for grayscale image enhancement in which the main interest is contrast enhancement. However, since colors usually have three attributes, including hue, saturation and intensity of more than only one attribute of grayscale values, the naive application of the methods for grayscale images to color images often results in unsatisfactory consequences. Conventional hue-preserving color image enhancement methods utilize histogram equalization (HE) for enhancing the contrast. However, they cannot always enhance the saturation simultaneously. In this paper, we propose a histogram specification (HS) method for enhancing the saturation in hue-preserving color image enhancement. The proposed method computes the target histogram for HS on the basis of the geometry of RGB (rad, green and blue) color space, whose shape is a cube with a unit side length. Therefore, the proposed method includes no parameters to be set by users. Experimental results show that the proposed method achieves higher color saturation than recent parameter-free methods for hue-preserving color image enhancement. As a result, the proposed method can be used for an alternative method of HE in hue-preserving color image enhancement.

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Section: Introductionmentioning

confidence: 99%

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

2017

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“…The model of [34], implemented in [37], settles on a multiple candidates selection strategy. The model is written in the Y U V color-space, and the target image (the image to colorize) is considered to be the luminance (Y ) channel of the expected color image.…”

Section: Variational Modelmentioning

confidence: 99%

“…The U and V chrominance channels corresponding to the pixel center of this retained patch is considered as a possible candidate. To speed-up the search of candidates, PatchMatch algorithm [3] has been implemented in [37]. The criterion used for the comparisons of the patches have been chosen experimentally, inspired from [11].…”

Section: Candidate Extractionmentioning

confidence: 99%

“…The criterion used for the comparisons of the patches have been chosen experimentally, inspired from [11]. The number and the variety of the criterion have been reduced from the original version, because it has been experimentally observed in the work of [37] that some criterion were especially low to compute and do not increase significantly the quality of the results. In the experiments, we have focused on four criterion:…”

Section: Candidate Extractionmentioning

confidence: 99%

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Inertial Alternating Generalized Forward–Backward Splitting for Image Colorization

2019

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In this paper, we propose a novel accelerated alternating optimization scheme to solve block-biconvex nonsmooth problems whose objectives can be split into smooth (separable) regularizers and simple coupling terms. The proposed method performs a Bregman distance based generalization of the well-known forward-backward splitting for each block, along with an inertial strategy which aims at getting empirical acceleration. We discuss the theoretical convergence of the proposed scheme and provide numerical experiments on image colorization.

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“…In [2], Beghdadi and Negrate gave a new contrast measure based on the edge detection operators and the visual perception criterion. In [22], the average local contrast measure was increased within a variational framework which preserved the hue of the original image by coupling the channels. In [23], Polesel et al introduced an adaptive filter which is used to control the enhancement in different regions.…”

mentioning

confidence: 99%

A variational gamma correction model for image contrast enhancement

Wang

Sun

Ng³

2019

Inverse Problems &Amp; Imaging

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Image contrast enhancement plays an important role in computer vision and pattern recognition by improving image quality. The main aim of this paper is to propose and develop a variational model for contrast enhancement of color images based on local gamma correction. The proposed variational model contains an energy functional to determine a local gamma function such that the gamma values can be set according to the local information of the input image. A spatial regularization of the gamma function is incorporated into the functional so that the contrast in an image can be modified by using the information of each pixel and its neighboring pixels. Another regularization term is also employed to preserve the ordering of pixel values. Theoretically, the existence and uniqueness of the minimizer of the proposed model are established. A fast algorithm can be developed to solve the resulting minimization model. Experimental results on benchmark images are presented to show that the performance of the proposed model are better than that of the other testing methods.

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Hue-preserving perceptual contrast enhancement

Cited by 9 publications

References 19 publications

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

RGB Color Cube-Based Histogram Specification for Hue-Preserving Color Image Enhancement

Inertial Alternating Generalized Forward–Backward Splitting for Image Colorization

A variational gamma correction model for image contrast enhancement

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