Markov random field-based image inpainting with direction structure distribution analysis for maintaining structure coherence

Image inpainting provides a means for restoration of an image with some damaged portions and has been widely applied to many fields. However, many existing inpainting methods still suffer the difficulties of effectively and efficiently maintaining structure coherence. To tackle the problems, this paper proposes a direction structure distribution analysis strategy for Markov Random Field (MRF) -based image inpainting algorithms. In the strategy, the desired direction structures (DDS) are selected and applied to guide the inpainting process. Given a degraded image, four kinds of direction edge information are extracted by super-wavelet transform, canny and dilation operators. Then local direction edge gradient magnitude is applied for direction structure distribution analysis and DDS are selected according to gradient variance. Afterwards, offsets of similar patches are calculated and only a few dominated offsets are chosen as candidate offsets. Finally, graph-cut optimization technology is utilized to solve energy function. Experimental results show that the proposed method achieves generally better performance than nine state-of-the-art methods in terms of the abilities of maintaining structure coherence and the computational cost on inpainting different kinds of degraded images.