Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Abstract

Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

“…The noise of HSIs can generally be divided into two types: systematic noise and random noise. The systematic noise is generated by the imperfect calibration of detectors, such as the stripes, which can be effectively removed by the state-of-the-art method [4][5][6][7]. The random noise is composed of electronic noise and photon noise.…”