论文标题
使用HAAR小波的近红外深度独立图像去悬
Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets
论文作者
论文摘要
我们提出了一种用于去除雾度的融合算法，该算法使用HAAR小波从其相应的NIR图像中提取的RGB图像和边缘信息结合了颜色信息。所提出的算法是基于关键观察，即在图像的朦胧区域中，NIR边缘特征比同一区域中的RGB边缘特征更为突出。为了结合颜色和边缘信息，我们引入了一张Haze权重图，该图在融合过程中比例分发了颜色和边缘信息。由于本质上是NIR图像几乎没有雾度，因此我们的作品没有像现有作品那样依赖散射模型并基本上设计独立的方法的假设。这有助于最大程度地减少工件，并为无雾图的图像具有更现实的感觉。广泛的实验表明，与现有的最新方法相比，在几个关键指标上，所提出的算法在定性和定量上都更好。
We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the RGB edge features in those same regions. To combine the color and edge information, we introduce a haze-weight map which proportionately distributes the color and edge information during the fusion process. Because NIR images are, intrinsically, nearly haze-free, our work makes no assumptions like existing works that rely on a scattering model and essentially designing a depth-independent method. This helps in minimizing artifacts and gives a more realistic sense to the restored haze-free image. Extensive experiments show that the proposed algorithm is both qualitatively and quantitatively better on several key metrics when compared to existing state-of-the-art methods.
