Moth Swarm Algorithm for Image Contrast Enhancement

Luque−Chang, Alberto; Cuevas, Erik; Pérez‐Cisneros, Marco; Fausto, Fernando; Valdivia-González, Arturo; Sarkar, Ram

doi:10.1016/j.knosys.2020.106607

Cited by 26 publications

(4 citation statements)

References 30 publications

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“…The target images and the predicted images are close in detail and contrast, and their subjective visual effects are similar. Table 1 shows the evaluation metrics, including contrast per pixel (CPP) [28], mean pixel contrast (MPC) [29], enhancement measure evaluation (EME) [30], image clarity (IC) [31], and entropy (E) [28]. Their formulas have the following representation:…”

Section: Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Zhong,

Fu,

Zhang

2023

Applied Sciences

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Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time.

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Section: Resultsmentioning

confidence: 99%

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Zhong,

Fu,

Zhang

2023

Applied Sciences

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“…Hence these schemes lead to a non-optimum redistribution of the pixel data under the presence of noise or an irrelevant set of pixels in the image. As a consequence, these approaches produce enhanced images with different problems, such as noise amplification or the generation of undesirable artifacts [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Image Contrast Enhancement Algorithm

2023

Self Cite

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One crucial step in several image processing and computer vision applications is Image Contrast Enhancement (ICE), whose main objective is to improve the quality of the information contained in the processed images. Most of the proposed schemes attack the problem by redistributing the pixel intensities in a histogram, leading to undesirable effects such as noise amplification, over-saturation, and lousy human perception. On the other hand, Agent-Based Models (ABM) are computational models that allow describing the behavior and interactions of autonomous agents when they operate cooperatively. These agents follow behavioral rules rather than mathematical formulations. This mechanism allows the implementation of complex behavioral patterns in agents through their interactions. This paper proposes a two-step method where pixels in the processed image are considered agents whose behavioral rules permit to enhance significatively the contrast. In our approach, the interactions among the agents are characterized by the differences in intensity values among the pixels or agents. In the first step, pixels or agents that present enough high differences in their intensity are modified to increase even more their differences. In the second step, pixels or agents that maintain a very small difference are altered to assume a homogeneous intensity value. The proposed approach has been tested considering different public datasets commonly used in the literature. Its results are also compared with those produced by other well-known ICE techniques. Evaluation of the experimental results demonstrates that the proposed approach highlights the important details of the image taking a lower computational execution time.INDEX TERMS Agent-based modeling, algorithms, complex systems, image contrast enhancement, image processing.

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“…The mentioned complex optimization problems can be effectively solved by Nature-Inspired Optimization Algorithms (NIOAs) using single or multiple objective functions [17] and different learning methods. NIOAs such as Artificial Bee Colony (ABC) [18], [19], Cuckoo Search (CS) [20], [21], Particle Swarm Optimization (PSO) [22], Firefly Algorithm (FA) [23], Wind-Driven Optimization (WDO) [24], Chicken Swarm Optimization (CSO) [25], and Moth Swarm Algorithm [26], [27] are effectively used in image processing. NIOAs have been also employed in variant applications such as image segmentation, classification, and compression [28], [29], [30].…”

Section: Introductionmentioning

confidence: 99%

Preventing Over-Enhancement Using Modified ICSO Algorithm

2023

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This paper proposes an Image Contrast Enhancement (ICE) method based on using an Improved Chicken Swarm Optimization (ICSO) algorithm to enhance images while at the same time preventing over-enhancement. In the optimization process, a new practical objective function is employed to reach three main goals, preserving the main details, generating an image with a uniform histogram, and reducing the spikes in the modified histogram. In the proposed approach, the RGB color channels are optimized individually. The performance of the proposed method is suitable for enhancing the contrast of low-and high-contrast images. A subjective experiment is designed to visually evaluate and compare the results with other ICE methods. The simulation results on the CSIQ, TID2013, and SEID datasets show that the proposed method outperforms numerous traditional and state-of-the-art ICE techniques both subjectively and objectively. The most important advantage of the newly proposed technique is that there is an agreement among observers on when over-enhancement occurs regardless of whether the Initial processed image was of low or high contrast. INDEX TERMSContrast enhancement, image quality assessment, over-enhancement.

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Moth Swarm Algorithm for Image Contrast Enhancement

Cited by 26 publications

References 30 publications

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Agent-Based Image Contrast Enhancement Algorithm

Preventing Over-Enhancement Using Modified ICSO Algorithm

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