Mine image enhancement using adaptive bilateral gamma adjustment and double plateaus histogram equalization

Li, Canlin; Liu, Jinhua; Zhu, Jinjuan; Zhang, Weizheng; Bi, Lei

doi:10.1007/s11042-022-12407-z

Cited by 10 publications

(7 citation statements)

References 31 publications

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“…However, for images with different grayscale values and display effects, the effect of gamma correction is single, and some important information may be missing. Therefore, this paper introduces the BIGA method [46] to process images through two gamma functions, which can improve the intensity of dark light while suppressing the enhancement of bright areas and can avoid excessive enhancement of color images. The global dual gamma correction expression is as follows:…”

Section: Adaptive Enhancement Of Low-illuminance Images Based On the ...mentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm’s performance with other swarm intelligence algorithms using commonly used test functions. The algorithm’s performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm.

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Section: Adaptive Enhancement Of Low-illuminance Images Based On the ...mentioning

confidence: 99%

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

Zhang,

Liu,

2024

Symmetry

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show abstract

“…We also can process the gathered images by LLIE methods. Histogram-equalization-based methods, including global histogram equalization (GHE) [ 16 , 17 ] and local histogram equalization (LHE) [ 3 , 4 , 5 ], directly adjust the image pixels value to redistribute their distribution in global and local levels. Swarm intelligence algorithms, image decomposition, Rayleigh distribution, and other technologies [ 31 , 32 , 33 ] were hired to optimize the previous HE-based approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Photos captured in insufficient illumination conditions such as nighttime, lopsided, under-exposed, etc., exhibit an undesired visual experience or deliver compromised messages for other computer vision tasks, due to their low contrast and lightness and blurry details [ 1 , 2 , 3 , 4 , 5 ]. Especially, high-level computer vision tasks show unsatisfactory performance in these low-light photos, such as in inaccurate face or object recognition [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image

Gong

Liu

et al. 2022

Sensors

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Low-illumination images exhibit low brightness, blurry details, and color casts, which present us an unnatural visual experience and further have a negative effect on other visual applications. Data-driven approaches show tremendous potential for lighting up the image brightness while preserving its visual naturalness. However, these methods introduce hand-crafted holes and noise enlargement or over/under enhancement and color deviation. For mitigating these challenging issues, this paper presents a frequency division and multiscale learning network named FDMLNet, including two subnets, DetNet and StruNet. This design first applies the guided filter to separate the high and low frequencies of authentic images, then DetNet and StruNet are, respectively, developed to process them, to fully explore their information at different frequencies. In StruNet, a feasible feature extraction module (FFEM), grouped by multiscale learning block (MSL) and a dual-branch channel attention mechanism (DCAM), is injected to promote its multiscale representation ability. In addition, three FFEMs are connected in a new dense connectivity meant to utilize multilevel features. Extensive quantitative and qualitative experiments on public benchmarks demonstrate that our FDMLNet outperforms state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability.

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“…Traditional techniques for improving low-light images in underground mine environments primarily include histogram equalization [6] and the Retinex model [5,7,8]. The former method is computationally efficient and enhances contrast by redistributing pixel greyscale values.…”

Section: Introductionmentioning

confidence: 99%

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

Han,

Xiao,

Yin

2024

IET Image Processing

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In recent years, low‐light image enhancement has become increasingly active. However, in underground mine environments, acquiring high‐quality images is still challenging due to low light, low contrast, and occlusion. To address this problem, this study proposes a low‐light image enhancement method for underground mine based on generative adversarial networks (UM‐GAN), which aims to take full advantage of the ability of GAN to achieve the restoration of details, the reduction of noise, and the improvement of overall image quality. The model proposed in this paper is divided into three main parts. Initially, a generator network adopting an encoder–decoder structure is developed. Subsequently, a novel strategy is introduced to merge information by utilizing inverted greyscale images and low‐light images as inputs. Further image quality enhancement is achieved by incorporating a noise reduction module employing the diffusion model. To ascertain the efficacy of the UM‐GAN method, evaluations are conducted on diverse real‐world and synthetic datasets, juxtaposing the approach against superior methods. Through qualitative and quantitative comparative experiments, the method showcases noteworthy advancements through qualitative and quantitative comparative experiments, substantiating its effectiveness. This research provides new ideas and methods for overcoming image quality problems in underground mine environments and contributes to the development of underground mine image processing.

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Mine image enhancement using adaptive bilateral gamma adjustment and double plateaus histogram equalization

Cited by 10 publications

References 31 publications

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

A Hybrid Swarming Algorithm for Adaptive Enhancement of Low-Illumination Images

FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

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