An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Lu, Haoxiang; Liu, Zhenbing; Pan, Xipeng

doi:10.1109/access.2020.3017499

Cited by 7 publications

(28 citation statements)

References 38 publications

(73 reference statements)

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“…Still, they perform unsatisfactorily in terms of robustness. Most significantly, [ 17 , 21 ] showed that the detail, edge, and noise were described in the high frequencies, while the main information was demonstrated in the low frequencies. A frequency division operation can extract feature maps at different frequencies to achieve the goal of preserving detail and compressing noise.…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…In past decades, a great deal of LLIE approaches, including histogram-based [ 3 , 16 , 17 ], Retinex-based [ 8 , 9 , 10 , 18 , 19 ], fusion-based [ 20 , 21 ], physical-model-based, [ 3 , 22 , 23 , 24 , 25 , 26 ] have been reported. Histogram-based methods, which are simple and highly efficient, introduce an over- or underenhancement owing to the spatial relationship among pixels being neglected.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gong

Liu

et al. 2022

Sensors

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“…Nevertheless, leakage and shadowed areas have overlapped luminance values. As a result, the spatial luminance-based techniques are not able to discern them [8], [25][26][27][28][29]. For TH=80, leakage areas can be identified in the segmented image as seen in Fig 6d).…”

Section: Ir Cameramentioning

confidence: 97%

“…When the thermogram is spatially low contrasted or displays small temporal changes, the visual inspection can fail to identify leakage areas. To resolve this problem, image processing techniques that are based on objective criteria (i. e. quantitative metrics) are applied [8,[24][25][26][27][28][29][30]. Histogram based…”

Section: Problem Formulation: Spatial-based Ir Leak Detectionmentioning

confidence: 99%

Non-Destructive Water Leak Detection Using Multitemporal Infrared Thermography

et al. 2021

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Water leakage detection and localization in distribution networks pipelines is a challenge for utility companies. For this purpose, thermal Infrared Radiation (IR) techniques have been widely applied in the literature. However, the classical analysis of IR images has not been robust in detecting and locating leakage, due to presence of thermal anomalies such as shadows. In this study, to improve the detection and location accuracy, a digital image processing tool based on multitemporal IR is proposed. In multitemporal IR analysis, the variation of soil's temperature due to field temperature can be obtained; and hence; estimating variations due to water leakage would be more accurate. An experimental setup was built to evaluate the proposed multitemporal IR water leak detection method. In order to consider the temporal temperature variation due to water leakage and mitigate the field temperature effects, a luminance transformation of the IR images was introduced. To determine the temporal temperature variation of the soil's surface due to the leakage, several metrics have been considered such as the difference, the ratio, the log-ratio and the coefficient variation (CV) images. Based on the experimental results, the log-ratio and the CV images were the most robust metrics. Then, based on log-ratio or the CV image, a temporal variation image (TVI) that traduces the temporal IR luminance variation was introduced. The analysis of the TVI image showed that the CV image is less noisy than the logratio image, and can more accurately locate the leakage. Finally, based on TVI histogram, a threshold was defined to classify the TVI image into leakage/non-leakage areas. Results showed that the proposed method is capable of accurately detecting and locating water leakage, which is an improvement to the false detections of spatial thermal IR analysis.INDEX TERMS Sustainable development goals (SDG), non-destructive evaluation (NDE), infrared thermography, water leak detection, multitemporal analysis.

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Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

Liu

Pan

et al. 2023

Vis Comput

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An Adaptive Detail Equalization for Infrared Image Enhancement Based on Multi-Scale Convolution

Cited by 7 publications

References 38 publications

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

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

Non-Destructive Water Leak Detection Using Multitemporal Infrared Thermography

Enhancing infrared images via multi-resolution contrast stretching and adaptive multi-scale detail boosting

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