Adaptive Image Dehazing and Object Tracking in UAV Videos Based on the Template Updating Siamese Network

Sun, Lifan; Chang, Jiashun; Zhang, Jinjin; Fan, Bo; He, Zishu

doi:10.1109/jsen.2023.3266653

Cited by 7 publications

(1 citation statement)

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“…G 2 a ðRÞ and G 2 b ðRÞ are partial derivatives of the image at R. G 1 ðRÞ and G 2 ðRÞ are the gradient modules of the dehaze and haze images, α is the adjustable parameters between the gradient module and transmission map. Several dehazing techniques 13,72,85,87,[132][133][134][135][136][137][139][140][141][142][143][144][145][146][147] introduce artifacts or distort the image, which reduces visibility. As an outcome, while the initial hazy images are original images without these degradations, some methods also have to consider the accuracy of the dehazing outcomes when evaluating a dehazing technique.…”

Section: Quantitative Measurementsmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Vision gets obscured in adverse weather conditions, such as heavy downpours, dense fog, haze, snowfall, etc., which increase the number of road accidents yearly. Modern methodologies are being developed at various academics and laboratories to enhance visibility in such adverse weather with the help of technologies. We review different dehazing techniques in many applications, such as outdoor surveillance, underwater navigation, intelligent transportation systems, object detection, etc. Dehazing is achieved in four primary steps: the capture of hazy images, estimation of atmospheric light with transmission map, image enhancement, and restoration. These four dehazing procedures allow for a step-by-step method for resolving the complicated haze removal issue. Furthermore, it also explores the limitations of existing deep learning-based methods with the available datasets and the challenges of the algorithms for enhancing visibility in adverse weather. Reviewed techniques reveal gaps in the case of remote sensing, satellite, and telescopic imaging. In the experimental analysis of various image dehazing approaches, one can learn the effectiveness of each phase in the image dehazing process and create more effective dehazing techniques.

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Section: Quantitative Measurementsmentioning

confidence: 99%

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Shit,

Ray

2023

J. Electron. Imag.

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Video Dehazing Network Based on Multiscale Attention

Han,

Zhang,

Brekhna

2023

IEEE Access

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Learning-based methods are mostly performed on images. However, less attention has been paid to these methods for video dehazing. This is because of the challenges involved in finding ways to better extract and fuse the spatial and temporal features between consecutive frames, as well as the reconstruction of a more realistic latent frame. This study proposes a multiscale attention video dehazing network (MAVDN) to recover clear dehazed videos. In terms of feature extraction, the proposed method used a feature extractor based on multiscale attention to extracting features at different levels comprehensively. A pixel attentionguided multi-frame fusion module was designed for temporal modeling to aggregate complementary information between adjacent frames. Additionally, a reconstruction module based on cascaded dilated convolutions was designed to better reconstruct the latent frames in the reconstruction stage. Extensive experiments conducted on real-world video dehazing datasets show that the proposed MAVDN achieved superior dehazing performance compared to state-of-the-art methods.

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A Gyro-Based Tracking Assistant for Drones With Uncooled Infrared Camera

Lee,

Kim

2024

IEEE Sensors J.

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Adaptive Image Dehazing and Object Tracking in UAV Videos Based on the Template Updating Siamese Network

Cited by 7 publications

References 32 publications

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

Video Dehazing Network Based on Multiscale Attention

A Gyro-Based Tracking Assistant for Drones With Uncooled Infrared Camera

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