Single Image Quality Improvement via Joint Local Structure Dehazing and Local Texture Enhancement

Remote sensing images are significantly degraded by bad weather conditions, such as haze and sandstorms, which provide unhelpful support for valuable information extraction. Most existing remote sensing image enhancement methods ignore the wavelength dependence of the scattering coefficient and local scattering differences of images, and therefore cannot well handle the colorized haze in which the medium transmission varies in different color channels. In this article, we propose a single image quality enhancement method using joint local structure dehazing and local texture enhancement (SDTE). Specifically, SDTE first uses a minimal channel between r, g, and b channels to estimate a coarse local airlight, and designs an achromatic airlight-driven refinement strategy to refine it. Meanwhile, SDTE estimates a local transmission via independent calculation of r, g, and b channels, which tackles the limitation that existing methods heavily depend on global transmission over the entire image. Then, SDTE removes the haze and amplifies the gradient using the estimated local airlight and transmission, thereby preserving significant structures and enhancing fine details. Finally, SDTE introduces an adaptive color correction based on the ranking of channel mean value and two channel-dependent gain factors to further eliminate the severe color distortion. More specially, we also collect a remote sensing colorized hazy image enhancement benchmark (RSCHI) including 339 remote sensing images captured in colorized haze or sandstorm, which makes it pay more attention to the color cast issue. We conduct a comprehensive study on benchmark datasets of RSCHI and UIEB and indicate better performance than the state-of-the-art (SOTA) methods. Meanwhile, we use a series of ablation studies to demonstrate the effectiveness and robustness of each key contribution and validate its generalization performance in other scenes.

I. Introduction

High quality remote sensing image plays an extremely important role in natural disaster monitoring, forest and mineral resources exploration, and military reconnaissance. However, images taken under bad scenes often suffer from low contrast, blurry detail, and color cast due to interference of suspended particles, which are unhelpful for extracting some valuable features of images, and further limit the understanding and analysis of remote sensing scenes. Thus, an effective method to enhance the image quality of diverse remote sensing scenes is desirable.