Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm

Hyperspectral images (HSI) are vulnerable to interference from the environment or the equipment during the acquisition process, causing a significant loss of remote sensing data. Therefore, hyperspectral image denoising is a fundamental issue in image preprocessing. In this paper, a denoising algorithm is designed, which divides HSI into local equal blocks and uses low-rank matrix constraints to characterize the local features. Moreover, the designed algorithm uses truncated nuclear norm minimization and global spatial-spectral total variation regularization to separate sparse and high-density noise, while maintaining spatial-spectral smoothness. The combination of the two methods can effectively remove mixed noises, including Gaussian and salt and pepper noises. The proposed optimization algorithm is compared with four recently published denoising algorithms, showing that the average structure similarity and average peak-signal-to-noise ratio are improved by 0.13 and 1.10 dB, respectively. Application of algorithms to a single noise with different intensity demonstrates that the average structure similarity is also improved by 0.10. The proposed method demonstrates a distinct noise removal effect in the amplification and contrast of actual images. Experimental results show that the proposed method is close to the local feature representation of hyperspectral images, which combined with the global regularization method, can facilitate a more obvious denoising effect and eliminate high-density and sparse noises.