Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets
This provides a depth-independent solution for improving image clarity in hazy conditions, beneficial for computer vision applications, though it is incremental as it builds on fusion techniques with NIR data.
The paper tackles image dehazing by fusing RGB color information with NIR edge features extracted via Haar wavelets, resulting in a method that outperforms state-of-the-art approaches on multiple metrics without relying on scattering models.
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.