tensorcompletion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low-rank nature of images, implicitly requiri...
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tensorcompletion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low-rank nature of images, implicitly requiring that the whole picture is of globally consistent features. As a result, existing tensor completion algorithms often give reasonably good performance if the target image has only random pixel-level missing. Unfortunately, pixel-level missing is very rare in practice and it is often wanted to restore an image with irregular hole-shaped missing, such as removing electricity poles from landscape photos or irrelevant people from tourist photos. This task is extremely difficult for traditional low-rank based tensorcompletion methods. To overcome this drawback, a Contour Information regularized tensor RIng completion (CITRIC) method is proposed for practical image restoration. Meanwhile, the contour information regularization is used to capture significant local features, whereas the low-rank tensor ring structure is utilized to capture as much global information as possible. The alternating direction method of multipliers (ADMM) is adopted to optimize the cost function. Extensive experimental results using real-world images show that CITRIC is more practical than existing methods and can restore real-world images with irregular hole-shaped missing.
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