A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting
| Autor: | Jeremy Trageser, Viktor Reshniak, Clayton G. Webster |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
I.4.4 business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Applied Mathematics General Mathematics Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inpainting Image (mathematics) 68U10 94A08 65D18 65K10 Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence business |
| Zdroj: | SIAM Journal on Imaging Sciences. 13:2140-2168 |
| ISSN: | 1936-4954 |
| Popis: | We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|