Remote Sensing Image Stripe Noise Removal: From Image Decomposition Perspective

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

“…Moreover, we compare the proposed method with four state-of-the-art destriping methods: filtering-based methods [16] (WAFT), statistics-based method [23] (SLD), optimization-based method [34] (GSLV) and from image decomposition perspective method [35] (LRSID) which also belongs to optimization-based methods. To highlight the destriping differences between the five compared methods, we mark some obvious differences by red circles or squares in the destriping images.…”

“…In remote sensing images stripe noise removal problem, the stripe effects can be regarded as additive noise [24,25,35], and the degradation model can be given by f(x, y) = u(x, y) + s(x, y) + n(x, y),…”

“…We use upper-case in bold letters for matrices (i.e., 2-D gray-level image), e.g., A. Mathematically, the matrix form of (1) can be extended as [3,35] …”

“…Based on the image decomposition model form [35], the stripe noise removal model for remote sensing images can be formulated as min U,S 1 2…”

“…To avoid the influence of randomness, we choose three different types of methods to extract the stripe component. The filtering-based method [16] (WAFT), statistics-based method [23] (SLD) and optimization-based method [35] (LRSID) are used to remove the stripe noise in MODIS band 33 image, and the results are shown in Figure 1. In the meantime, the stripe component is obtained by the difference between the observed image and image component for WAFT and SLD methods, and LRSID method estimates the stripe component by its stripe removal model.…”