Image inpainting techniques based on deep learning have shown significant improvements by introducing structure priors, but still generate structure distortion or textures fuzzy for large missing areas. This is mainly because series networks have inherent disadvantages: employing unreasonable structural priors will inevitably lead to severe mistakes in the second stage of cascade inpainting framework. To address this issue, an appearance flow-based structure prior (AFSP) guided image inpainting is proposed. In the first stage, a structure generator regards edge-preserved smooth images as global structures of images and then appearance flow warps small-scale features in input and flows to corrupted regions. In the second stage, a texture generator using contextual attention is designed to yield image high-frequency details after obtaining reasonable structure priors. Compared with state-of-the-art approaches, the proposed AFSP achieved visually more realistic results. Compared on the Places2 dataset, the most challenging with 1.8 million high-resolution images of 365 complex scenes, shows that AFSP was 1.1731 dB higher than the average peak signal-to-noise ratio for EdgeConnect.
At present, two-stage networks are widely used in image restoration methods, but existing two-stage network often generates inpainting results with distorted structures and blurry textures, especially when reconstructed object is more complex. The main reason is insufficient structure prior and inaccurate, which leads to generating wrong results in texture generation stage. In order to solve this problem, a novel Image Inpainting based on Edge and Smooth Structures Prediction is proposed. The edge structure and smooth structure are completed in structure reconstruction stage, and reconstructed edge structure and smooth structure are simultaneously used as a prior to guide texture generation stage fills in damaged area. The proposed method is evaluated on publicly available datasets Paris StreetView, CelebA-HQ and Places2, and many experiments show that proposed method obtains excellent results under subjective and objective indexes compared with mainstream approaches.
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