Name
Abstract | ||
|---|---|---|
Partial differential equations (PDEs) have emerged as a useful tool for recovering structures when solving image inpainting problems. In the research presented in this paper, we extend structure tensor (ST)-based PDE to fine-grained texture inpainting by improving the ST with three modifications to enhance its suitability to more accurately process fine-grained textures of which the small-scale patterns repeat non-locally. These modifications are then inserted into an anisotropic PDE to recover damaged textures. The result is a subtly-conducted anisotropic diffusion inpainting algorithm free of detail-blurring artifacts produced by the classic tensor diffusion model and dislocation deficiencies affecting patch-based completion approaches. This is made possible by the following strategies: (1) construct a fractional ST (FST 1 1Fractional structure tensor.
) composed of the inner product of the fractional derivative vector and its transposition to more effectively address complex fractal-like texture details because of the characteristics of the fractional differential; (2) because FST is composed of the inner product of two vectors, it is necessary to represent the tensor at a higher resolution than for the original image to avoid spectrum aliasing, which is automatically implemented by the calculation of the twofold oversampling fractional derivative (i.e., double-sampling FST, DFST 2 2Double-sampling fractional structure tensor.
); (3) apply a non-local regularizing method to all four channels of the resulting DFST based on the repeatability of textures, which integrates non-local geometric characteristics to effectively infer how damaged textures extend. Finally, we insert the modified non-locally regularized DFST (NDFST 3 3Nonlocally-regularized double-sampling fractional structure tensor.
) into an anisotropic PDE to effectively guide the diffusion process and design its numerical implementation scheme. The experimental results show that, for fine-grained texture images, the proposed NDFST is equipped to accurately detect subtle and complex texture features, and the resulting NDFST-based PDE is particularly effective not only for recovering non-homogeneous structures, but also for the restitution of fine-grained textures. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.image.2018.02.006 | Signal Processing: Image Communication |
Keywords | Field | DocType |
Texture,Inpainting,Structure tensor,Non-local,Fractional-order | Anisotropic diffusion,Computer vision,Tensor,Oversampling,Computer science,Algorithm,Inpainting,Aliasing,Artificial intelligence,Fractional calculus,Structure tensor,Partial differential equation | Journal |
Volume | ISSN | Citations |
73 | 0923-5965 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuhong Yang | 1 | 1 | 1.71 |
| Baolong Guo | 2 | 1 | 1.71 |
| Zhaolin Xiao | 3 | 4 | 5.15 |
| Wei Liang | 4 | 22 | 10.29 |