An Infrared Image Enhancement Algorithm for Gas Leak Detecting Based on Gaussian Filtering and Adaptive Histogram Segmentation

Nie, Xiaoxi; Zhang, Hanlong; Líu, Hao; Liang, Yan; He, Wei; Luo, Dong; Chen, Wei

doi:10.1109/rcar52367.2021.9517369

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Another approach preserves thermal target data via multi-threshold segmentation based on maximum entropy and refines the brightness and contrast of visible images employing a retina-inspired brightness correction technique [17]. Nie et al [18] devise a method that amalgamates adaptive histogram segmentation and Gaussian filtering, achieving efficacious denoising and the amplification of gas leakage traces.…”

Section: Thermal Image Enhancementmentioning

confidence: 99%

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Fu,

Shi,

et al. 2024

Meas. Sci. Technol.

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Traditional vision-based inertial odometry suffers from significant visual degradation, which substantially impacts state estimation in challenging lighting environments. Thermal imaging cameras capture images based on the thermal radiation of objects, rendering them impervious to lighting variations. However, integrating thermal infrared data into conventional visual odometry poses challenges due to its low texture, poor contrast, and high noise levels. In this paper, we propose a tightly coupled approach that seamlessly integrates information from visible light cameras, thermal imaging cameras, and inertial measurement units. First, we employ adaptive bilateral filtering and Sobel gradient enhancement to smooth infrared images, thereby reducing noise and enhancing edge contrast. Second, we leverage the Sage-Husa adaptive filter in conjunction with iterative Kalman filtering (IEKF) to effectively mitigate the impact of non-Gaussian noise on the system. Finally, we conduct comprehensive evaluations of the proposed system using both open datasets and real-world experiments across four distinct scenarios: normal lighting, low-light conditions, low-light conditions with camera shake, and challenging lighting environments. Comparative analysis reveals that our method outperforms IEKF, yielding a reduction in localization error measured by root mean square error by 58.69%, 57.24%, 60.23%, and 30.87% in these respective scenarios.

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Section: Thermal Image Enhancementmentioning

confidence: 99%

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Fu,

Shi,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Scene-based calibration methods solve the recalibration problem by continuously updating the calibration parameters based on the imaging results. Common algorithms include constant statistical [ 11 ], time-domain Gaussian filtering [ 12 ], Kalman filtering [ 13 ], etc. However, most of these correction methods have to obtain parameters based on the results of the previous frame or several frames.…”

Section: Related Workmentioning

confidence: 99%

Visible-Image-Assisted Nonuniformity Correction of Infrared Images Using the GAN with SEBlock

Mou

Zhu

Zhou

2023

Sensors

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Aiming at reducing image detail loss and edge blur in the existing nonuniformity correction (NUC) methods, a new visible-image-assisted NUC algorithm based on a dual-discriminator generative adversarial network (GAN) with SEBlock (VIA-NUC) is proposed. The algorithm uses the visible image as a reference for better uniformity. The generative model downsamples the infrared and visible images separately for multiscale feature extraction. Then, image reconstruction is achieved by decoding the infrared feature maps with the assistance of the visible features at the same scale. During decoding, SEBlock, a channel attention mechanism, and skip connection are used to ensure that more distinctive channel and spatial features are extracted from the visible features. Two discriminators based on vision transformer (Vit) and discrete wavelet transform (DWT) were designed, which perform global and local judgments on the generated image from the texture features and frequency domain features of the model, respectively. The results are then fed back to the generator for adversarial learning. This approach can effectively remove nonuniform noise while preserving the texture. The performance of the proposed method was validated using public datasets. The average structural similarity (SSIM) and average peak signal-to-noise ratio (PSNR) of the corrected images exceeded 0.97 and 37.11 dB, respectively. The experimental results show that the proposed method improves the metric evaluation by more than 3%.

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Design of Lightweight Infrared Image Enhancement Network Based on Adversarial Generation

Pan

et al. 2022

2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)

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An Infrared Image Enhancement Algorithm for Gas Leak Detecting Based on Gaussian Filtering and Adaptive Histogram Segmentation

Cited by 5 publications

References 15 publications

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Tightly coupled visual-inertial fusion with image enhancement for robust positioning

Visible-Image-Assisted Nonuniformity Correction of Infrared Images Using the GAN with SEBlock

Design of Lightweight Infrared Image Enhancement Network Based on Adversarial Generation

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