Adaptive Wavelet Attention-Guided Tensor Completion with Fused Low-Rankness and Smoothness for Hyperspectral Image Restoration

Low-rank tensor completion is widely used to recover missing data in hyperspectral images. However, existing methods often rely on fixed low-rank models and hand-crafted priors, which may fail to preserve spatial and spectral structures when large amounts of data are missing. To address this issue, we propose Adaptive Wavelet Attention-guided Smooth Tensor Completion (AWSTC), an adaptive wavelet attention-guided deep unfolding framework for recovering missing entries in hyperspectral image tensors by jointly exploiting low-rankness and smoothness. AWSTC models tensor completion as a low-rank factorization problem with fused smoothness regularization. Its adaptive wavelet attention module learns the importance of different channels from wavelet subbands to guide low-rank recovery. The resulting optimization algorithm is unfolded into a multi-stage network that combines model-based updates with learnable residual dense blocks. Experiments on three hyperspectral image datasets show that AWSTC performs well across various missing rates, producing clearer images with fewer stripe artifacts.