Compared-neighborhood based image dehazing for improved visibility

Abstract

Image hazing is the degradation of photographic quality due to light attenuation by mist or suspended particles. This paper presents solutions to two shortcomings of existing haze-removal techniques. One shortcoming is that pixels often get zeroed out in the dehazing process, which suppresses edges and features. The other is that the assumption of homogeneity of features and properties in input images during dehazing reduces the resolution of features and textures. A solution that considers how feature and edge visibility, which are primarily disrupted by noise, is provided. This noise is responsible for the lack of distinction between local and global pixel neighborhoods. An attenuation coefficient that helps to minimize pixel distortion is proposed. This coefficient is sensitive to local relative pixel intensity and prevents the pixels from being zeroed out in the context of certain local or global neighborhoods. The proposed technique is implemented via the existing dual-stream network based on a CNN with the block-greedy algorithm. The qualitative and quantitative evaluation based on 117 images shows 75% improvement in haze density ζ, 90% increase in edge visibility e, and 150% improvement in the peak-signal-to-noise ratio (PNR), and 95% increase in structural similarity index measure (SSIM) compared to the original hazed image. These show a remarkable improvement compared to the existing state-of-the-art methods. The shortcoming is a need for color improvement, which can be studied further in future studies.