Hyperspectral image denoising with sparse spatial-spectral transformer
The application of Transformer models has improved the performance of hyperspectral image denoising.However,the original Transformer model still falls short in effectively leveraging the spatial-spectral coupling in HSIs.It tends to excessively smooth spatial features,leading to the loss of small-scale structures.Moreover,it overly emphasizes all spectral channel features,neglecting the differences between different spectral bands.In order to solve these problems,this paper introduces a novel Sparse Spatial-Spectral Transformer model,enhancing the utilization of spatial-spectral coupling.In the spatial dimension,a local enhancement module is introduced to refine spatial feature details and deal with over-smoothing problem.Simultaneously,in the spectral dimension,a Top-k sparse self-attention mechanism is proposed,which adaptively selects the top-K most relevant spectral channel features for feature interaction,effectively capturing spatial-spectral characteristics.Ultimately,hyperspectral image denoising is achieved through hierarchical residual connections with the Sparse Spatial-Spectral Transformer.On the ICVL dataset,denoising performance for both Gaussian noise and complex noise attains peak signal-to-noise ratios of 40.56 dB and 40.19 dB,respectively,demonstrating the superior performance of the proposed Sparse Spatial-Spectral Transformer model in this paper.
万方数据
维普
