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Sparse-view computed tomography (CT) is one of the primary means to reduce the radiation risk. But the reconstruction of sparse-view CT will be contaminated by severe artifacts. By carefully designing the regularization terms, the iterative reconstruction (IR) algorithm can achieve promising results. With the introduction of deep learning techniques, learned regularization terms with convolution neural network (CNN) attracts much attention and can further improve the performance. In this paper, we propose a learned local-nonlocal regularization-based model called RegFormer to reconstruct CT images. Specifically, we unroll the iterative scheme into a neural network and replace handcrafted regularization terms with learnable kernels. The convolution layers are used to learn local regularization with excellent denoising performance. Simultaneously, transformer encoders and decoders incorporate the learned …