PROJECT TITLE :
Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping
Multispectral remote sensing images often suffer from the common drawback of stripe noise, which greatly degrades the imaging quality and limits the precision of the following processing. The traditional destriping approaches sometimes remove stripe noise band by band, and show their limitations on completely different types of stripe noise. During this paper, we have a tendency to tentatively categorize the stripes in remote sensing images during a more comprehensive manner. We tend to propose to treat the multispectral pictures as a spectral-spatial volume and cause an anisotropic spectral-spatial total variation regularization to reinforce the smoothness of resolution along each the spectral and spatial dimension. Therefore, a a lot of comprehensive stripes and random noise are perfectly removed, while the sides and detail info are well preserved. Additionally, the split Bregman iteration methodology is used to unravel the ensuing minimization problem, which highly reduces the computational load. We tend to extensively validate our technique underneath various stripe categories and show comparison with alternative approaches with respect to result quality, running time, and quantitative assessments.
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