Haze Removal for a Single Remote Sensing Image Using Low-Rank and Sparse Prior

Abstract

Due to the influence of atmospheric scattering, the quality of remote sensing images is degraded, which severely limits the utility of remote sensing images. In this article, a novel dehazing algorithm for a single remote sensing image is proposed based on a low-rank and sparse prior (LSP). According to an atmospheric scattering model, the dark channel of a hazy image is decomposed into two parts: the dark channel of direct attenuation with sparseness and the atmospheric veil with low rank. The prior is obtained from the overall decomposition of the image rather than the patches of the image; therefore, the image pixel changes of the local blocks have little influence on the prior. Considering different resolutions of remote sensing images, the calculations of blocks involved in this article are completed by adaptive methods. The principal component pursuit and alternating direction multiplier method (PCP-ADMM) combined with the adaptive threshold shrinkage method are used for low-rank and sparse decomposition, therefore, the coarse estimation of the atmospheric veil is obtained. The guided filter with adaptive radius is used to refine it, and then the accurate atmospheric light is estimated. Finally, using the deformed atmospheric scattering model based on the atmospheric veil and atmospheric light, the haze-free image is restored. Extensive experimental results on publicly available data sets show that the dehazed images have abundant detail, high contrast, and minimal color distortion when using the proposed method, which is competitive with most state-of-the-art technologies.