An Adaptive Iterative Inpainting Method with More Information Exploration

The CNN-based image inpainting methods have achieved promising performance because of its outstanding semantic understanding and reasoning potentialities. However, previous works could not get satisfied results in some situations because information is not fully explored. In this paper, we propose a new method by combining three innovative ideas. First, to increase the diversity of the semantic information obtained by the network in image synthesis, we propose a multiple hidden space perceptual (MHSP) loss, which extracts high-level features from multiple pre-trained autoencoders. Second, we adopt an adaptive iterative reasoning (AIR) stategy to reduce the calculations under small-hole circumstances while ensuring the performance in large-hole circumstances. Third, we find that color inconsistencies occasionally occurred in the final image merging process, so we add a novel interval maximum saturation (IMS) loss to the final loss function. Experiments on the benchmark datasets show our method performs favorably against state-of-the-art approaches. Code is made publicly available at: https://github.com/IC-LAB/adaptive_iterative_inpainting.