Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Image Inpainting Based on Coherence Transport
Journal of Mathematical Imaging and Vision
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
A New Spatial Hue Angle Metric for Perceptual Image Difference
Computational Color Imaging
Image Inpainting Based on Local Optimisation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
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Improved digital image inpainting algorithms could provide substantial support for future artwork restoration. However, currently, there is an acknowledged lack of quantitative metrics for image inpainting evaluation. In this paper the performance of eight inpainting algorithms is first evaluated by means of a psychophysical experiment. The ranking of the algorithms thus obtained confirms that exemplar based methods generally outperform PDE based methods. Two novel inpainting quality metrics, proposed in this paper, eight general image quality metrics and four inpainting-specific metrics are then evaluated by validation against the perceptual data. Results show that no metric can adequately predict inpainting quality over the entire image database, and that the performance of the metrics is image-dependent.