利用偏振光扩散原理，无需外部光源即可去雾并增强细节。
A Polarization Image Dehazing Method Based on the Principle of Physical Diffusion
- 基于物理扩散机制设计偏振图像去雾模型
- 通过傅里叶变换与反卷积恢复雾滴状态和光照分布
- 适合户外、水下等复杂场景的实时成像应用
计算机视觉在无人驾驶、监控系统和遥感等领域应用日益广泛。然而，在雾霾环境下，图像退化导致目标细节丢失，严重影响视觉任务的准确性和有效性。偏振光因其电磁波振动方向特定，相比非偏振光更不易受复杂介质中的散射和折射影响，能在长距离成像中保持更好的偏振特性。这一优势使偏振成像特别适用于户外和水下等复杂场景，尤其在雾霾环境中可获取更高品质图像。为此，本文提出一种创新的半物理偏振去雾方法，无需外部光源。该方法模拟雾的扩散过程，设计对应图像模糊的扩散核，结合时空傅里叶变换与反卷积操作，恢复雾滴扩散前的状态及物体的光照反演分布，有效实现场景去雾与细节增强。
原文摘要 · Abstract (English)
Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electromagnetic waves vibrate in a specific direction, is able to resist scattering and refraction effects in complex media more effectively compared to unpolarized light. As a result, polarized light has a greater ability to maintain its polarization characteristics in complex transmission media and under long-distance imaging conditions. This property makes polarized imaging especially suitable for complex scenes such as outdoor and underwater, especially in foggy environments, where higher quality images can be obtained. Based on this advantage, we propose an innovative semi-physical polarization dehazing method that does not rely on an external light source. The method simulates the diffusion process of fog and designs a diffusion kernel that corresponds to the image blurriness caused by this diffusion. By employing spatiotemporal Fourier transforms and deconvolution operations, the method recovers the state of fog droplets prior to diffusion and the light inversion distribution of objects. This approach effectively achieves dehazing and detail enhancement of the scene.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。