期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 24, 期 6, 页码 1852-1866出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2404782
关键词
Destriping; denoising; spectral-spatial total variation; split Bregman iteration; remote sensing image
资金
- Fundamental Research Funds for the Central Universities, Huazhong University of Science and Technology, Wuhan, China [2013TS131]
- National Natural Science Foundation of China [61433007, 60902060]
Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner. We propose to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral-spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension. As a result, a more comprehensive stripes and random noise are perfectly removed, while the edges and detail information are well preserved. In addition, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. We extensively validate our method under various stripe categories and show comparison with other approaches with respect to result quality, running time, and quantitative assessments.
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