Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly

Prasath, R.; Kumanan, T.

doi:10.1093/comjnl/bxab056

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Before enhancing the image, it is necessary to convert the high brightness image into a low brightness image with a brightness value within the range of [0, 1 − I th ]. The expression for the brightness limited image can be obtained as shown in equation (11). (11) In equation (11), I lim represents the brightness limited image of the V channel in the HSV space.…”

Section: B Image Brightness Equalization Algorithm Based On Illuminat...mentioning

confidence: 99%

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Image Enhancement and Brightness Equalization Algorithms in Low Illumination Environment Based on Multiple Frame Sequences

Yan

2023

IEEE Access

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Images captured in low illumination environments can affect people's judgment of image information. To raise the visual effect of low illumination images and preserve the integrity of image information, this study proposes two algorithms. One is an image enhancement algorithm for low illumination, which combines the WASOBI image denoising method for deep denoising of images and adaptive gamma correction for brightness adjustment of images. The other is an image brightness equalization algorithm for uneven lighting. This algorithm divides the image according to the illumination area, and uses guided filtering and adaptive gamma correction to equalize the brightness of the image. The laboratory outcomes demonstrate that the proposed image enhancement algorithm can effectively reduce the noise level of the image and adjust the brightness of the image, and the algorithm has a certain degree of stability. The subjective evaluation score of the image processed using the brightness equalization algorithm is 4.7, indicating that the image has good visual effects. The objective evaluation results prove the capability of the equilibrium algorithm, and comparing to other algorithms, the algorithm has the shortest operation time, only 0.9452 seconds. Therefore, the enhancement algorithm and brightness equalization algorithm proposed in the article have certain application value.

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Section: B Image Brightness Equalization Algorithm Based On Illuminat...mentioning

confidence: 99%

“…The expression for the brightness limited image can be obtained as shown in equation (11). (11) In equation (11), I lim represents the brightness limited image of the V channel in the HSV space. I v represents all images of the V channel in the HSV space.…”

Section: B Image Brightness Equalization Algorithm Based On Illuminat...mentioning

confidence: 99%

Image Enhancement and Brightness Equalization Algorithms in Low Illumination Environment Based on Multiple Frame Sequences

Yan

2023

IEEE Access

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“…This method aims to pinpoint the best histogram for VR scene detail amplification, boosting contrast, and color richness in VR images. Testing outcomes reveal that this approach has unique benefits in refining VR visuals [14]. However, this method cannot guarantee good results in dynamic range compression and contrast enhancement at the same time.…”

Section: Introductionmentioning

confidence: 99%

VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm

Feng,

Tong,

Zhu

et al. 2024

Int J Comput Intell Syst

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In virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex changes, making it difficult to capture environmental modeling relationships and reducing the quality of scene details. Therefore, a VR scene detail enhancement method based on deep reinforcement learning algorithm is proposed. Using the Total Variation (TV) denoising algorithm to decompose the VR scene, the VR scene is divided into two parts: noisy and non-noisy, to complete the noise removal. Based on the denoised VR scene, a Hessian matrix is constructed to extract VR scene features using the SURF algorithm. Using deep reinforcement learning algorithms to train and process the extracted VR visual features, introducing meta-learning within the gradient descent method, updating the parameters of the deep reinforcement learning Expose framework, and accelerating the training speed of the deep reinforcement learning Expose framework. By designing L1 loss, structural similarity loss, content perception loss, and sharpness loss functions, the enhancement effect of VR visual details can be improved. The experimental results show that the proposed method enhances the gray-scale values and distribution of VR scene detail images to be higher and more uniform. When the magnification is different, the studied method can effectively enhance the signal-to-noise ratio of VR scenes. Therefore, it indicates that the VR scene detail enhancement effect of the method proposed in this article is good.

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RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment

Thakur

Goraya

2022

Simulation Modelling Practice and Theory

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Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly

Cited by 5 publications

References 35 publications

Image Enhancement and Brightness Equalization Algorithms in Low Illumination Environment Based on Multiple Frame Sequences

Image Enhancement and Brightness Equalization Algorithms in Low Illumination Environment Based on Multiple Frame Sequences

VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm

RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment

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