Multispectral Image Denoising via Structural Tensor Sparsity Promoting Model

Multispectral images (MSIs) contain more spectral information than traditional 2D images, which can provide a more accurate representation of objects. MSIs are easily affected by various noises when captured by sensors. In recent years, many MSI denoising methods, especially the Kronecker-basis-representation (KBR) method, have achieved great success. KBR uses tensor representation and decomposition to achieve good MSI denoising performance. However, each full band patch (FBP) group is decomposed in this method so that too many dictionary atoms are generated. In this paper, we propose a structural tensor sparsity promoting (STSP) model for MSI denoising. In order to decrease the number of dictionary atoms, we cluster FBP groups and learn orthogonal dictionaries for each class rather than each FBP group. To improve the denoising performance, the structural similarity among FBP groups are utilized in the STSP model by enforcing nonlocal centralized sparse constraint, where the compromise parameter is statistically and adaptively determined. Experimental results on the the CAVE dataset demonstrate that our model outperforms the state-of-art methods in terms of both objective and subjective quality.