ASAUN-CS: Adaptive Stage Activated Unfolding Network for Compressive Sensing

Recently, numerous deep unfolding networks (DUN) based compressive sensing (CS) methods have been proposed to combine the neural network with conventional optimization algorithms and implemented by a stack of recurrent trainable stages. To obtain high-quality recovered images, it is common practice to employ identical iteration stages for all images. However, in the case of straightforward images with limited high-complexity information, a low number of iterations already yields satisfactory quality. Extending the iteration stages does not notably enhance the quality but results in increased reconstruction time. In this paper, we propose an adaptive stage activated unfolding network (ASAUN-CS), mainly including a measurement assessment module in the compressed domain and a stage selection module in the feature domain. By the proposed method, the images with different recovered complexity can adaptively select the stage number in the unfolding subnet, which reduces the computational time significantly. Comprehensive experimental results demonstrate that the proposed framework obtains satisfying reconstructed performance while using fewer parameters and faster processing speed.