Deep Plug-and-play Prior for Low-rank Tensor Completion
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Tensor image data sets such as color images and multispectral images are highly correlated and they contain a lot of image details. The main aim of this paper is to propose and develop a regularized tensor completion model for tensor image data completion. In the objective function, we adopt the newly emerged tensor nuclear norm (TNN) to characterize the global structure of such tensor image data sets. Also, we formulate an implicit regularizer to plug in the convolutional neural network (CNN) denoiser, which is convinced to express the image prior learned from a large amount of natural images. The resulting model can be solved efficiently via an alternating directional method of multipliers algorithm. Experimental results (on color images, videos, and multispectral images) are presented to show that both image global structure and details can be recovered very well, and to illustrate that the performance of the proposed method is better than that of testing methods in terms of PSNR and SSIM.
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