A multi-scale fusion scheme based on haze-relevant features for single image dehazing

Abstract

Abstract Outdoor images are often degraded by aerosols suspending in atmosphere in bad weather conditions like haze. To cope with this phenomenon, researchers have proposed many approaches and single image based techniques draw attention mostly. Recently, a fusion-based strategy achieves good results, which derives two enhanced images from single image and blends them to recover haze-free image. However, there are still some deficiencies in the fusion-input images and weight maps, which leads their restoration less natural. In this paper, we propose a multi-scale fusion scheme for single image dehazing. We first use an adaptive color normalization to eliminate a common phenomenon, color distortion, in haze condition. Then two enhanced images, including our newly presented local detail enhanced image, are derived to be blended. Thereafter, five haze-relevant features of dark channel, clarity, saliency, luminance and chromatic are investigated since those can serve as weight maps for fusion. Dark channel, clarity and saliency features are finally selected due to their expression abilities and less interconnection. The fusion is processed with a pyramid strategy layer-by-layer. The multi-scale blended images are combined in a bottom-up manner. At last quantitative experiments demonstrate that our approach is effectiveness and yields better results than other methods.