Spectral Extension of Hyperspectral Images with Convolutional Sparse Representation

Hyperspectral images (HSIs) capture rich spatial-spectral information and are widely used in various fields. However, the spectral ranges of hyperspectral imaging cameras are generally limited to a narrow range, like visible (VIS) or near-infrared (NIR), due to the constraints of optical elements and imaging sensors. Existing methods primarily focus on enhancing the visual effect in the spatial or spectral domain, neglecting the correlation between different spectral ranges. Therefore, this work aims to accurately correlate HSIs across different spectral ranges, enabling efficient spectral extension using algorithms. To achieve spectral extension, we explore the sparsity of real-world HSIs and propose a sparse representation framework based on the spectral convolutional dictionary. This approach reveals latent features shared across different spectral ranges, which are effectively captured by learned convolutional dictionaries and their associated sparse coefficients. Building upon this representation, we introduce the Unfolding Convolutional Sparse Representation Transformer (UCSRT) model, jointly optimizing the convolutional dictionaries and sparse coefficients. The Spectral Coding Transformer in the UCSRT achieves accurate and efficient sparse encoding through a sparse self-attention mechanism guided by spectral sparsity. Additionally, Spectral Convolutional Dictionary Learning Module is proposed to learn the convolutional dictionary for the VIS-NIR spectral range. This framework enables the extension of HSIs from a narrow VIS range to a wider VIS-NIR range. Extensive experiments on real-world datasets demonstrate the effectiveness and accuracy of our spectral extension method. Comparative analyses with state-of-the-art spectral reconstruction models, as well as practical applications for small target detection in disguised environments and enhancement in snapshot hyperspectral imaging, further highlight the superiority of our UCSRT method.