Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
This work addresses image enhancement for digital media, but it appears incremental as it builds on existing deep learning approaches without specifying major breakthroughs.
The paper tackles image inpainting by proposing a U-Net-like network that extracts features and replaces damaged pixels with recovered ones, resulting in high-quality outcomes compared to traditional methods.
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.