| Popis: |
For a damaged image, the loss of pixel information can be roughly divided into two categories, random missing and non-random missing. The missing of an entire row or column of the image is a specific structural missing pattern that is extremely difficult to deal with. Although most of the existing methods have partially fixed this information missed problem, the diffusion-based methods tend to produce blur, the exemplar-based methods are prone to error filling, and the neural network-based methods are highly dependent on data, which cannot handle this special structural missing very well. Using the nonlocal self-similarity prior and the low-rank prior, we present multidirectional search and nonlocal low-rank tensor completion (MS-NLLRTC) algorithm based on the tensor ring (TR) decomposition and multidirectional search (MS). The MS method is a newly proposed method that can search similar patches much more diversified. Using MS method, we directly stack the similar patches into a three-dimensional similar tensor instead of pulling them into column vectors, then the similar tensor can be completed by TR decomposition. The optimization results can be obtained by leveraging the alternating direction method under the augmented Lagrangian multiplier framework. Moreover, we add a weighted nuclear norm to the tensor completion model (WNLLRTC), achieving a better inpainting performance. We also combine a noise removal method with WNLLRTC algorithm, which can handle image random missing and image noise removal simultaneously. Experimental results indicate that our proposed algorithms are competitive with some state-of-the-art inpainting algorithms in terms of both numerical evaluation and visual quality. |
