How does an AI-powered algorithm enhance visibility in smoky and hazy environments?
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An AI-powered algorithm enhances visibility in smoky and hazy environments by using deep learning-based dehaze models and image synthesis techniques. These algorithms can remove haze from images and improve imaging quality . They can learn the features of hazy images and generate clear dehaze images by applying the learned model to the original hazy images . Additionally, these algorithms can synthesize hazed images using cycle generative adversarial networks (CycleGAN) to create well-pixel-aligned synthesized hazy and clear paired images for training . By removing haze and enhancing image quality, these AI-powered algorithms can improve visibility in smoky and hazy environments, making it easier to detect objects, navigate, and ensure safety in various applications such as autonomous driving, smart cities, and intelligent transportation systems .
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| Papers (5) | Insight |
|---|---|
21 Citations | The paper proposes a deep network-enabled three-stage dehazing network (TSDNet) that uses a multiscale attention module, two-branch extraction module, and multifeature fusion module to enhance visibility in hazy environments. |
| The provided paper proposes a method for enhancing visibility in hazy environments using a cycle generative adversarial network (CycleGAN) to synthesize paired hazy and clear images for training deep networks. | |
| The provided paper proposes an AI scheme that uses snapshot NIR hyperspectral data to detect objects in smoky and hazy environments, improving visibility in search and rescue operations. | |
| The AI-powered algorithm enhances visibility in smoky and hazy environments by controlling the brightness of the marine lantern based on the concentration of sea fog, autonomously adjusting the light intensity to improve visibility. | |
| The provided paper does not mention the use of an AI-powered algorithm to enhance visibility in smoky and hazy environments. The paper focuses on using a Bayesian inference model to classify visibility values using 3D-LiDAR point clouds. |
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