Adaptive image inpainting algorithm based on sample block by kriging pretreatment and facet model

We present an improved Criminisi image inpainting algorithm through kriging pretreatment and facet model. We propose three improvement strategies. First, we propose an improved priority. A priority used in Criminisi algorithm consists of confidence term and data term. As iteration progresses, the confidence term is updated and plays a key role in the confidence term of the next edge block. At the same time, the confidence term gradually tends to 0, leading to a weakening of the role of priority, and ultimately affecting the repair effect. We propose an improved priority, which is represented by a piecewise function. Importantly, this improved priority reduces the risk of being weakened during iteration. Second, Criminisi algorithm uses fixed-size sample blocks to repair damaged images, regardless of whether the image content is a textured area or a structure area. We introduce an adaptive method to select sample block size based on the facet model. The size of the sample block is adaptively adjusted for different image contents, thereby improving the quality of the repaired image. Third, we show a weighted sum of squares differences matching principle based on the facet model. The matching formula is determined by the pixel gray value of a sample block and the structure value of four directions, which improves matching accuracy between target block and optimal matching block. Finally, experimental results show that the proposed algorithm is competitive with some state-of-the-art inpainting techniques in terms of both objective metrics and subjective visual inspection.