SST: self-attention transformer for infrared deconvolution

This study addresses the challenge of enhancing denoising in infrared spectroscopy signals and proposes a novel method based on the sparse self -attention model. In the domain of long sequence spectrum deconvolution problem, traditional Transformers confront challenges, encompassing quadratic time intricacy, heightened utilization of memory, and restrictions posed by the encoder-decoder architecture. To address these concerns, we present sparse self -attention mechanisms and extraction procedures, effectively handling the quadratic time complexity within Transformers. Additionally, a carefully designed generative decoder is utilized to alleviate the constraints of the traditional encoder-decoder architecture. Applied to the restoration of infrared spectra, our proposed method yields satisfactory results. Leveraging the sparse self -attention model, we successfully achieve enhanced denoising of infrared spectroscopy signals, providing a novel and effective approach for long sequence time series prediction. Experimental findings showcase the extensive applicability of this approach in the field of infrared spectroscopy.