Texture-Aware Deblurring for Remote Sensing Images Using <inline-formula><tex-math notation="LaTeX">$ \ell _0$</tex-math></inline-formula>-Based Deblurring and <inline-formula><tex-math notation="LaTeX">$ \ell _2$</tex-math></inline-formula>-Based Fusion

oleh: Heunseung Lim, Soohwan Yu, Kwanwoo Park, Doochun Seo, Joonki Paik

Format: Article
Diterbitkan: IEEE 2020-01-01

Deskripsi

This article presents an image deblurring method using &#x2113;<sub>0</sub>-norm-based deblurring and &#x2113;<sub>2</sub>-norm-based texture-aware image fusion for remote sensing images. To restore the details of blurred texture, the proposed method first performs texture restoration by fusing the restored results using Richardson-Lucy deconvolution and unsharp masking. Next, we analyzed the intensity and dark channel properties of remote sensing images and perform the &#x2113;<sub>0</sub>-norm-based deblurring using the intensity and dark channel priors. Although the &#x2113;<sub>0</sub>-norm-based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed &#x2113;<sub>2</sub>-norm-based image fusion method can preserve both sharp edges and texture details. In the experiments, we demonstrate that the proposed method can provide better restored results than existing state-of-the-art deblurring methods without oversmoothing and undesired artifact.