Deep unfolding (DU) methods constitute a promising class of neural networks that have shown good performance in image reconstruction and denoising for compressive hyperspectral imaging. Existing DU methods, however, have difficulties in balancing inference speed and reconstruction accuracy due to their limitations in the information transmission capacity, degradation estimation method, denoiser structure and training strategy. This paper proposes a novel DU architecture, dubbed degradation-estimated hybrid unfolding transformer network (DHUTNet), to improve the reconstruction quality and inference speed simultaneously. Firstly, a novel degradation-estimated hybrid unfolding framework (DHUF) is proposed to improve the generalization ability and training efficiency. The recovery module in DHUF includes a linear projection branch and a deep recovery branch. Moreover, the deep recovery branch estimates a pixel-specific degradation pattern from the projection error in each stage. Secondly, a dense-prior fusion module is designed to connect different stages of DHUF to further improve the reconstruction quality with less computation burden. Thirdly, a Ushape hybrid-attention transformer is introduced in the denoising module of DHUTNet to efficiently model various correlations of hyperspectral images. Finally, a novel progressive inherited training strategy for the larger-version DHUTNet is proposed, which significantly reduces the training time and improves the reconstruction quality. Experimental results demonstrate that the proposed DHUTNet outperforms existing methods, and can improve the inference speed and training efficiency up to 5-fold and 21-fold, but still achieve equivalent reconstruction quality.

Coded aperture snapshot spectral imager (CASSI) can recover three-dimensional hyperspectral images (HSIs) from two-dimensional compressive measurements. Recently, deep unfolding approaches were shown impressive reconstruction performance among various algorithms. Existing deep unfolding methods usually employ linear projection methods to guide the iterative learning process. However, the linear projections do not include trainable parameters and ignore the essential characteristics of HSI. This paper proposes a novel learning-based deep estimation-enhancement unfolding (DEEU) framework to improve the HSI reconstruction. The deep estimation-enhancement (DEE) module is used to guide the iterative learning process of the network based on the prior information of the CASSI system, and then exploits the intrinsic features of the reconstructed HSI in both spectral and spatial dimensions. In addition, a multi-prior ensemble learning module is proposed to further improve the reconstruction performance without increasing runtime. As with most of deep unfolding methods, we plug a convolutional neural network as a denoiser in each stage of the DEEU framework, which finally forms the proposed DEEU-Net. Comprehensive experiments on both simulation and real datasets demonstrate that the effectiveness of our DEEU framework, and our DEEU-Net can achieve both high reconstruction quality and speed, outperforming the state-of-the-art methods.