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Image restoration is a popular and challenge task, which is regarded as a classical inverse problem. Condat-V ũ primal-dual algorithm based on proximal operator is one of successful optimization methods. It is further reformulated as a primal-dual proximal network, where one iteration in the original algorithm corresponds to one layer in the network. The drawback of primal-dual network is that blur kernels should be given as prior information, however, it is usually very hard to be known in the real situation. In this work, we propose a deep encoder-decoder primal-dual proximal network, named ED-PDPNet. In each layer, the blur kernels and the projections between the primal and dual variables are designed as encoder-decoder modules, in this way, the network can be learned in an end-to-end way and all the parameters in the primal-dual algorithm are learned. The proposed method is applied on the MNIST and BSD68 datasets for image restoration. The preliminary results show that the proposed method by combining simple encoder-decoder modules obtained very promising and competitive performance compared to the state-of-the-art methods. In addition, the proposed network is shown to be a lightweight network with fewer learning parameters in comparison to the recent popular transformer-based method.
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