Abstract: In view of the problems of existing sparse iterative imaging methods such as limited resolution in complex scenes with low signal-to-noise ratio and unknown noise characteristics, reliance on manual parameter settings, and poor noise adaptability, this paper proposes an adaptive iterative super-resolution imaging method based on the deep unfolding network SLIM-Net. This method maps the iterative process of the iterative sparse learning minimization (SLIM) method into a trainable multi-layer deep network structure, uses convolutional neural networks to realize the joint dynamic estimation of signal power and noise power, and enhances gradient propagation through residual connections to ensure the stable convergence of the deep network. Through end-to-end training, the network can adaptively learn the optimal parameters and adapt to complex noise and low signal-to-noise ratio environments. Experimental and simulation results show that SLIM-Net can significantly improve the resolution and robustness in low signal-to-noise ratio and multi-target environments, has a faster convergence speed and higher imaging accuracy, and improves the performance of the SLIM method by about 32.8% under low signal-to-noise ratio.