Multi-Scale Fusion of Enhanced Hazy Images Using Particle Swarm Optimization and Fuzzy Intensification Operators

Abstract

Dehazing from a single image is still a challenging task, where the thickness of the haze depends on depth information. Researchers focus on this area by eliminating haze from the single image by using restoration techniques based on haze image model. Using haze image model, the haze is eliminated by estimating atmospheric light, transmission, and depth. A few researchers have focused on enhancement based methods for eliminating haze from images. Enhancement based dehazing algorithms will lead to saturation of pixels in the enhanced image. This is due to assigning fixed values to the parameters used to enhance an image. Therefore, the enhancement based methods fail in the proper tuning of the parameters. This can be overcome by optimizing the parameters that are used to enhance the images. This paper describes the research work carried to derive two enhanced images from a single input hazy image using particle swarm optimization and fuzzy intensification operators. The two derived images are further fused using multi-scale fusion technique. The objective evaluation shows that the entropy of the haze eliminated images is comparatively better than the state-of-the-art algorithms. Also, the fog density is measured using an evaluator known as fog aware density evaluator (FADE), which considers all the statistical parameters to differentiate a hazy image from a highly visible natural image. Using this evaluator we found that the density of the fog is less in our proposed method when compared with enhancement based algorithms used to eliminate haze from images.