An investigation of various Dehazing algorithms used on thermal infrared imagery for maritime surveillance systems

Mansoori, Abdulla Al; Swamidoss, Issac Niwas; Almarzooqi, Abdulrahman; Sayadi, Slim

doi:10.1117/12.2599684

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Over the course of the past several years, numerous applications of thermal cameras in machine vision have been developed [52]- [59]. These applications includes object recognition, human activity detection, visual odometry and Simultaneous Localization And Mapping (SLAM) [60][61][62] Thermal sensors are one type of imaging technology that, in certain conditions, can function even when there is no visible light.…”

Section: Thermal Camera Visionmentioning

confidence: 99%

Analysis of modern learning-based multimodal fusion algorithms for neuromorphic vision sensors

AlRemeithi¹,

Swamidoss²,

Mansoori³

et al. 2023

SPIE Future Sensing Technologies 2023

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Image enhancement is an ongoing research problem that the community is addressing through the development of fusion algorithms. Such techniques typically involve the reconstruction of RGB images by removing environmental artifacts and enhancing desired features. Infrared imagery is also widely used to improve situational awareness in low visibility scenarios. Recently, learning-based approaches are used for fusion purposes to extract meaningful representations from images and capture latent features that could otherwise be inaccessible using conventional image processing algorithms. The inadequacies of RGB images in these algorithms’ pipelines are still obvious, despite the fact that the viability of RGB-Infrared image fusion has been thoroughly demonstrated in the literature. For example, RGB images often have artefacts like sudden changes in exposure or motion blur when the illumination changes or sudden changes in the scene. A novel imaging sensor operating in the visible light spectrum has been developed to address these issues. In this paper, we explore the cutting-edge paradigm of Neuromorphic Vision Sensors (NVS), a class of asynchronous analog imaging platforms that operate based on the change of pixel luminosity within a scene. When compared to frame-based counterparts, NVS enhances scene interpretation, processing time, reaction time, and power consumption. Deep-learning reconstruction networks are evaluated in this study to determine the applicability of existing state-of-the-art multi-modal image fusion techniques with the addition of NVS data rather than RGB data. As a benchmark, metrics such as signal-to-noise ratio (SNR) and pixel wise error are used.

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Section: Thermal Camera Visionmentioning

confidence: 99%

Analysis of modern learning-based multimodal fusion algorithms for neuromorphic vision sensors

AlRemeithi¹,

Swamidoss²,

Mansoori³

et al. 2023

SPIE Future Sensing Technologies 2023

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“…Existing systems may have algorithms to evaluate uniformity. Long-range thermal imaging systems [7] need to capture a wide range of temperature values. Evaluating the dynamic range ensures that the thermal camera can capture a wide range of temperature values without saturating or losing details.…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment of thermal images for Maritime surveillance applications

Swamidoss,

Al Mansoori,

Shajahan

et al. 2024

SPIE Future Sensing Technologies 2024

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Maritime surveillance systems employing thermal imaging encounter numerous challenges, where image quality significantly affects their effective range of vision. Adverse weather conditions such as haze, fog, and smog can obscure thermal imaging scenes, complicating the detection, identification, and tracking of objects of interest. For instance, these systems must track moving ships from a considerable distance using thermal imaging, while adapting to dynamic backgrounds and various weather conditions. Image quality assessment, a crucial research area, evaluates the perceived quality of an image. Standards for quantifying images often align with human perception, adopting user-focused approaches that consider an observer's ability to perform specific tasks, as outlined in the Johnson criteria. However, in real-time maritime surveillance applications, these criteria may prove inadequate in capturing image properties. This study explores the general factors that measure the dynamic range of marine surveillance thermal images, along with specific challenges in interpreting images using various quality assessment parameters.

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Thermal Infrared Single Image Dehazing and Blind Image Quality Assessment

Erlenbusch,

Merkt,

de Oliveira

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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An investigation of various Dehazing algorithms used on thermal infrared imagery for maritime surveillance systems

Cited by 5 publications

References 13 publications

Analysis of modern learning-based multimodal fusion algorithms for neuromorphic vision sensors

Analysis of modern learning-based multimodal fusion algorithms for neuromorphic vision sensors

Image quality assessment of thermal images for Maritime surveillance applications

Thermal Infrared Single Image Dehazing and Blind Image Quality Assessment

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