Near-Infrared Depth-Independent Image Dehazing using Haar Wavelets

TL;DR

This paper introduces a depth-independent haze removal method that fuses RGB and NIR images using Haar wavelets, improving haze removal quality without relying on scattering models.

Contribution

The work presents a novel fusion algorithm leveraging NIR edge features for haze removal, avoiding depth assumptions and reducing artifacts.

Findings

Outperforms existing methods on key metrics

Produces more realistic haze-free images

Effective in haze regions with prominent NIR edges

Abstract

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.