Low-rank tensor embedded deep neural network for hyperspectral image denoising
Spectral imagery has emerged as a powerful tool with widespread applications across various fields.This tool's unique ability to identify materials continuous narrow bands has made it invaluable for tasks such as environmental monitoring,resource management,and agricultural early warning.However,the hyperspectral imagery utility is often compromised by the presence of noise from factors such as equipment errors and atmospheric conditions.This noise poses a significant challenge to the accuracy of subsequent analytical tasks,requiring the development of effective hyperspectral image denoising techniques.The spatial,spectral,and spatial-spectral joint correlations observed in hyperspectral images indicate that clean hyperspectral images occupy a low-dimensional subspace.This characteristic can be effectively characterized by low-rank and sparse representations.Consequently,a considerable body of research has been dedicated to exploring denoising methods based on such representations to enhance hyperspectral image quality.On the one hand,while deep learning methods offer the advantage of directly extracting prior information from data,they often exhibit low efficiency in the utilization of physical knowledge specific to hyperspectral images,such as their inherent low-rank nature.On the other hand,model-based techniques require the manual setting of priors and intricate parameter tuning,presenting a challenge in terms of practicality and adaptability.The objective of this study is to address the existing challenges in hyperspectral image denoising by proposing a novel approach that combines the strengths of deep learning-and model-based methods.The proposed methodology leverages the spatial-spectral low-rank characteristics inherent in hyperspectral images and embeds the low-rank tensor decomposition module into the U-Net for enhanced denoising.The low-rank tensor decomposition module is based on CP decomposition,generates rank-one vectors,and reconstructs low-rank tensors through operations like global pooling and convolution.The low-rank tensor decomposition module is integrated with the U-net architecture to represent shallow features as low-rank tensors.This strategy enables the model to capture spatial-spectral low-rank characteristics comprehensively,thereby significantly enhancing its denoising capabilities.Experimental evaluations,encompassing both simulated and real data,validate the efficacy of the proposed low-rank deep neural network method.Across a noise standard deviation range of[0-95],the algorithm achieves a peak signal-to-noise ratio of 41.02 dB and a structural similarity index of 0.9888.Empirical results underscore the superiority of the proposed low-rank deep neural network method over alternative approaches in terms of denoising performance for hyperspectral images.By effectively leveraging the spatial-spectral low-rank characteristics intrinsic to hyperspectral images,this methodology presents a robust solution for enhancing the accuracy of hyperspectral imagery in diverse applications.The amalgamation of low-rank tensor decomposition with deep learning techniques not only addresses existing challenges but also opens up promising avenues for future research in hyperspectral image processing,paving the way for improved methodologies and innovative solutions.The comprehensive exploration of this combined approach provides valuable insights and contributes to the evolving landscape of hyperspectral image analysis and enhancement.
hyperspectral image denoisingdeep neural networklow-rank tensor representationknowledge-driven deep learningCP decompositionU-Net
万方数据
维普
