Transformed Structured Sparsity With Smoothness for Hyperspectral Image Deblurring

Due to the influence of imaging equipment or environment, a hyperspectral image (HSI) is often unavoidably blurred in the acquisition process, which results in the spatial and spectral information loss of the HSI. The existing HSI deblurring methods can address the problem, however, they neglect the intrinsic structured sparsity and thus reduce the deblurring performance. Aiming at this issue, we propose a new HSI deblurring method based on transformed structured sparsity with smoothness (TSSS). We first use the local piecewise smoothness to obtain the spatial and spectral sparsity of an HSI in the gradient domain. Then, to capture the refined sparsity, we exploit the transform sparsity learning framework to encode the structured sparsity self-adaptively in transform space, where the sparse structures of transformed operators can be depicted by Laplacian scale mixture (LSM), i.e., the sparsity can be expressed as the product of a hidden positive scalar multiplier and a Laplacian vector. The visual and quantitative comparisons of experimental results on three HSI datasets indicate that our method outperforms state-of-the-arts.