  Deep unfolding methods and transformer architecture have recently shown
promising results in hyperspectral image (HSI) reconstruction. However, there
still exist two issues: (1) in the data subproblem, most methods represents the
stepsize utilizing a learnable parameter. Nevertheless, for different spectral
channel, error between features and ground truth is unequal. (2) Transformer
struggles to balance receptive field size with pixel-wise detail information.
To overcome the aforementioned drawbacks, We proposed an adaptive step-size
perception unfolding network (ASPUN), a deep unfolding network based on FISTA
algorithm, which uses an adaptive step-size perception module to estimate the
update step-size of each spectral channel. In addition, we design a Non-local
Hybrid Attention Transformer(NHAT) module for fully leveraging the receptive
field advantage of transformer. By plugging the NLHA into the Non-local
Information Aggregation (NLIA) module, the unfolding network can achieve better
reconstruction results. Experimental results show that our ASPUN is superior to
the existing SOTA algorithms and achieves the best performance.
