Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

Matin, Farzin; Jeong, Yoosoo; Kim, K; Park, Kyong

doi:10.1088/1742-6596/960/1/012026

Cited by 12 publications

(12 citation statements)

References 5 publications

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“…Thus, the image is enhanced while retaining the original color distribution, and the grayish color typically observed in images enhanced using the MSRCR algorithm is effectively improved [128]. Later, Matin et al optimized the MSRCP method using particle swarm optimization (PSO) to avoid manual adjustment of the parameters [129]. Chen and Beghdadi [130] proposed an image enhancement algorithm based on Retinex and a histogram stretch method to maintain the natural color of images.…”

Section: ) Other Retinex Algorithmsmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: ) Other Retinex Algorithmsmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…We also experimented with various image processing methods and techniques that are commonly used for data augmentation in CNN such as noise injection, image rotation, color jittering, horizontal, and vertical flips, and random cropping [17]. We also tried some special image processing filters such as Gabor, bilateral filter, median filter, Retinex, Msrcp, and Automated-Retinex [19,20]. However, none of these improved the accuracy of test results or helped us to avoid the overfitting problem.…”

Section: Design and Implementationmentioning

confidence: 99%

A convolutional neural network model for marble quality classification

Karaali

Eminağaoğlu

2020

SN Appl. Sci.

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The fundamental policy of marble industries is to establish sustainable high-quality products in a standardized manner. Identification and classification of different types of marbles is a critical task that is usually carried out by human experts. However, marble quality classification by humans can be time-consuming, error-prone, inconsistent, and subjective. Automated and computerized approaches are required to obtain faster, more reliable, and less subjective results. In this study, a deep learning model is developed to perform multi-classification of marble slab images with six different quality types. Blur filter, 5 ✕ 5 low-pass 2D linear separable convolution filter using Gaussian kernel, and erosion filter were applied to the images for data augmentation, and a special convolutional neural network (CNN) architecture was designed and implemented. It has been observed that the data augmentation approach for marble image samples has significantly improved the accuracy of the CNN model ranging between 0.922 and 0.961.

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“…However, the retinex method requires control parameters to be tuned and the parameters are often manually and heuristically set [3]. To automatically tune the control parameters of the retinex method, we can employ particle swarm optimization (PSO) [4] as in [3], [5] because PSO is simpler to implement, faster in comparison with other optimization methods and also ideal for non-linear problems. PSO measures Manuscript received June 10, 2020.…”

Section: Introductionmentioning

confidence: 99%

Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function

Matin

Jeong

Park

2020

IEICE Trans. Inf. & Syst.

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Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.

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Color Image Enhancement Using Multiscale Retinex Based on Particle Swarm Optimization Method

Cited by 12 publications

References 5 publications

An Experiment-Based Review of Low-Light Image Enhancement Methods

An Experiment-Based Review of Low-Light Image Enhancement Methods

A convolutional neural network model for marble quality classification

Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function

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