Abstract
Hyperspectral (HS) imaging captured at low proximity ranges has become an effective method for assessing the quality and safety of food products in recent years. The majority of Hyperspectral cameras are push-broom type, which often contributes random noise and stripes to the spectral images. As a result, the quality of the collected HS images and the precision of the subsequent processing will both suffer. Despite the fact that there are several denoising techniques that have been studied and recorded in the literature, they frequently fail to fully restore the original HS images when there are strong stripe noises and other intricate mixed noises. In this study, we introduce a new method based on tensor logarithmic Schatten-p norm minimization with anisotropic spatial spectral total variation (ASSTV) regularization for simultaneous denoising and destriping of line scan-based hyperspectral images. Tensor logarithmic Schatten-p norm improves denoising performance by deleting the smaller singular values while protecting the larger singular values. Through the application of ASSTV, stripes and Gaussian noise are eliminated while maintaining the spatial–spectral smoothness of HS images and the directional characteristics of the stripes. The augmented Lagrange multiplier (ALM) approach is used to address the ensuing optimization problem. The quantitative and visual assessments reveal that the suggested technique has improved performance than its counterparts.
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