Saturation-Based Airlight Color Restoration of Hazy Images

Abstract

Typically, images captured in adverse weather conditions such as haze or smog exhibit light gray or white color on screen; therefore, existing hazy image restoration studies have performed dehazing under the same assumption. However, hazy images captured under actual weather conditions tend to change color because of various environmental factors such as dust, chemical substances, sea, and lighting. Color-shifted hazy images have hindered accurate color perception of the images, and due to the dark haze color, they have worsened visibility compared to conventional hazy images. Therefore, various color correction-based dehazing algorithms have recently been implemented to restore colorcast images. However, existing color restoration studies are limited in that they struggle to distinguish between haze and objects, particularly when haze veils and images have a similar color or when objects with a high saturation value occupy a significant portion of the scene, resulting in overly grayish images and distorted colors. Therefore, we propose a saturation-based dehazing method that extracts only the hue of the cast airlight and preserves the information of the object. First, the proposed color correction method uses a dominant color extraction method for the clustering of CIELAB(LAB) color images and then assigns area scores to the classified clusters. Sorting of the airlight areas is performed using the area score, and gray world-based white balance is performed by extracting the hue of the area. Finally, the saturation of the restored image is used to separate and process the distant objects and airlight, and dehazing is performed by applying a weighting value to the depth map based on the average luminance. Our color restoration method prevents excessive gray tone and color distortion. In particular, the proposed dehazing method improves upon existing issues where near-field information is lost and noise is introduced in the far field as visibility improves.