Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
Isfahan University of Technology（伊斯法罕理工大学）；McMaster University（麦克马斯特大学）
英文摘要：
Image inpainting is an effective method to enhance distorted digital images. Different inpainting methods use the information of neighboring pixels to predict the value of missing pixels. Recently deep neural networks have been used to learn structural and semantic details of images for inpainting purposes. In this paper, we propose a network for image inpainting. This network, similar to U-Net, extracts various features from images, leading to better results. We improved the final results by replacing the damaged pixels with the recovered pixels of the output images. Our experimental results show that this method produces high-quality results compare to the traditional methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
