A variational gamma correction model for image contrast enhancement

Wang, Wei; Sun, Na; Ng, Michael K.

doi:10.3934/ipi.2019023

Cited by 20 publications

(13 citation statements)

References 29 publications

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“…[30][31][32] Gamma correction allows the programmer to operate in a linear intensity space. [30][31][32] Gamma correction allows the programmer to operate in a linear intensity space.…”

Section: Literature Reviewmentioning

confidence: 99%

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Using chaos world cup optimization algorithm for medical images contrast enhancement

Wang

Liu

Jimenez

2019

Concurrency and Computation

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In this paper, a chaos-based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state-of-the-art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.

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“…[30][31][32] Gamma correction allows the programmer to operate in a linear intensity space. [30][31][32] Gamma correction allows the programmer to operate in a linear intensity space.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To cover the aforementioned shortcomings, Gamma correction was suggested by different researchers. [30][31][32] Gamma correction allows the programmer to operate in a linear intensity space. This linear space is more intuitive than a nonlinear space.…”

Section: Literature Reviewmentioning

confidence: 99%

Using chaos world cup optimization algorithm for medical images contrast enhancement

Wang

Liu

Jimenez

2019

Concurrency and Computation

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“…Therefore, improving the contrast to better view the image pictorial features, reveal the latent details, and improve the representation of information is a primary requirement [11]. Image processing procedures related to image contrast enhancement are usually involved [12]. More specifically, this is done by applying a reliable contrast enhancement method, which plays a key role in improving the perceived quality without generating unwanted processing artifacts [13].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Al-Ameen¹

2021

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Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.

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“…Currently, there are different types of medical image contrast enhancement (14,15). The application of the Gamma correction for the contrast enhancement of the medical images is widespread; the main reason for this is its ability in preserving the brightness (16)(17)(18)(19). Other methods for medical image enhancement include waveletbased enhancement (20), histogram equalization (HEs) (21), 2D empirical mode decomposition (22), decorrelation stretching methods (23), PDE-based (24), and median filterbased methods (25).…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

Zhou¹,

Shi²,

Lai³

et al. 2019

Quant. Imaging Med. Surg.

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Background: In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm.Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient.Methods: In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization.Results: The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed.Conclusions: Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.

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A variational gamma correction model for image contrast enhancement

Cited by 20 publications

References 29 publications

Using chaos world cup optimization algorithm for medical images contrast enhancement

Using chaos world cup optimization algorithm for medical images contrast enhancement

Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

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