Content-Adaptive Unfolding Wavelet Transformer for Hyperspectral Image Super-Resolution

In recent years, fusing high-resolution multispectral images (HR-MSIs) and low-resolution hyperspectral images (LR-HSIs) has become a widely used approach for hyperspectral image super-resolution (HSI-SR). The deep unfolding framework has attracted significant attention thanks to its ability to formulate the problem into a data module and a prior module. However, there are still two critical issues that hinder the performance enhancement of the existing methods: 1) Parameters in the data module are fixed (though learnable) at each iteration, i.e., lacking the adaptivity to comprehensive data; 2) The Transformer in the prior module cannot effectively capture high-frequency information. To resolve these issues, we propose a Content-Adaptive Unfolding Wavelet Transformer (CAUWT) for HSI-SR, where the parameters are adaptively learned based on the reconstructed HSI at each iteration. Moreover, we propose a novel Wavelet-Assisted Transformer (WAT), by integrating the Discrete Wavelet Transform (DWT) and the Hybrid Spectral-Spatial Attention Block (HSSAB) to further upgrade the high-frequency information quality of HSI at no cost of extra branch structures, where the former is for multi-scale and multi-frequency details and the latter is for correlations between and within sub-band components. Extensive experiments performed on both simulated and real datasets well demonstrate the effectiveness of the proposed method. In comparison with mainstream HSI-SR methods, our method exhibits superior performance and lower computational overhead.