Zero-Shot Learning-Based Haze Removal Through Near-Infrared and Color Images Fusion

Abstract

In recent years, zero-shot learning-based haze removal methods using a single image have been proposed and have gained attention for their effectiveness. However, methods that fuse near-infrared (NIR) and color images have not been sufficiently studied. This paper presents a haze removal method based on zero-shot learning that fuses NIR and color images. The proposed method consists of two steps: haze removal and edge fusion. In the first step, the atmospheric scattering model is adapted to remove haze from NIR and color images. This step restores colors in the color image and enhances edges in the NIR image. In the second step, a new method is introduced to fuse haze-removed NIR and color images. This method preserves the natural color and the luminance of the color image and effectively uses the edges of the NIR image. Specifically, a weight map is generated to adjust for luminance changes and is added to the NIR image. The adjusted NIR image is then multiplied by the lightness image to restore the edges. This process allows for a natural fusion of NIR and lightness images and an effective fusion of detailed edges. Our qualitative and quantitative evaluations demonstrated that our method can restore color and edges more naturally than the conventional methods. Furthermore, it was shown to be effective even for strong haze images.