Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Inpainting with sparse linear combinations of exemplars
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Image inpainting via sparse representation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Image inpainting by patch propagation using patch sparsity
IEEE Transactions on Image Processing
Orientation-Matching Minimization for Image Denoising and Inpainting
International Journal of Computer Vision
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Nonlocal Mumford-Shah Regularizers for Color Image Restoration
IEEE Transactions on Image Processing
A Primal–Dual Method for Total-Variation-Based Wavelet Domain Inpainting
IEEE Transactions on Image Processing
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To better maintain structure coherence and neighborhood consistency, an exemplar-based inpainting algorithm is presented by introducing color-gradient patch sparsity (CGPS). Two concepts of CGPS including color-gradient structure sparsity (CGSS) and patch sparse representation are proposed to obtain the filling order, the search region size and the sparse representation of target patch, which are key steps in an exemplar-based inpainting algorithm. Firstly, the CGSS is designed based on weighted color-gradient distance (WCGD) to determine the filling order of all patches located at fill-front. Secondly, the WCGD is applied to search candidate patches and the CGSS is used to limit the search region size. Thirdly, the patch to be filled is sparsely represented under the local patch consistency constraints in color and gradient spaces. Differing from the exemplar-based inpainting approaches in which only color information is used, the proposed algorithm considers both color and gradient information, which ensures a better maintenance of structure coherence, texture clarity and neighborhood consistency. Moreover, the inpainting efficiency can be significantly improved by limiting the search region size via the CGSS. Experimental results on natural images are presented to demonstrate the advantages of the proposed approach for various tasks such as scratch removal, text removal, block removal and object removal.