Patch-Based Dark Channel Prior Dehazing for RS Multi-spectral Image

Remote sensing(RS) multi-spectral images are usually suffered from cloud and fog cover, which can lead to analysis troubles and application limitations. A novel patch-based dark channel prior dehaze method is proposed for solving this problem. An Atmospheric light (AL) curved surface hypothesis, instead of globally invariable plane, is applied to describe AL distribution, and a patch-based approach is given to estimate curved surface. By using AL curved surface estimation, a new recovering model for RS multi-spectral images is given to obtain dehaze-free images. Comparative experiments are conducted, those results illustrate that the proposed method can produces visually impressive restored images, and the proposed method is superior to other relative methods in terms of image quality evaluations.