Texture-Aware Deblurring for Remote Sensing Images Using [Formula Omitted]-Based Deblurring and [Formula Omitted]-Based Fusion

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; p. 3094
• Main Authors: Lim, Heunseung, Yu, Soohwan, Park, Kwanwoo, Seo, Doochun, Paik, Joonki
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01-01-2020
• Subjects: Computer vision; Detection; Image processing; Image restoration; Masking; Remote sensing; Restoration; Texture
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2999961

This article presents an image deblurring method using [Formula Omitted]-norm-based deblurring and [Formula Omitted]-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 [Formula Omitted]-norm-based deblurring using the intensity and dark channel priors. Although the [Formula Omitted]-norm-based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed [Formula Omitted]-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.