Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
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Abstract (arXiv)
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.
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References · 10
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
- 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
- 2019EdgeConnect: Generative Image Inpainting with Adversarial Edge Learningcited by 163
- 2019Progressive Image Inpainting with Full-Resolution Residual Networkcited by 19
- 2018Structural inpaintingcited by 7
- 2019Image Inpainting by Adaptive Fusion of Variable Spline Interpolationscited by 1
- 2020Image Inpainting by Multiscale Spline Interpolationcited by 1
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