Summary of Image Inpainting For Corrupted Images by Using the Semi-super Resolution Gan, By Mehrshad Momen-tayefeh et al.
Image inpainting for corrupted images by using the semi-super resolution GAN
by Mehrshad Momen-Tayefeh, Mehrdad Momen-Tayefeh, Amir Ali Ghafourian Ghahramani
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel Generative Adversarial Network (GAN) for image inpainting, which learns to replicate missing pixels in corrupted images. The primary challenge is addressing the extent of corruption, which the model must restore. To achieve this, the authors introduce the Semi-Super-Resolution GAN (SSRGAN), a variant of the Super-Resolution GAN (SRGAN). The proposed model leverages three diverse datasets to assess robustness and accuracy. The training process involves varying levels of pixel corruption to optimize accuracy and generate high-quality images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image inpainting helps fix corrupted pictures. This paper uses deep learning to fill in missing parts. They created a new GAN that learns to restore images with different amounts of damage. They also made an updated version called Semi-SRGAN, which is based on Super-Resolution GAN. The team tested their model using three different sets of pictures and adjusted the training process to get the best results. |
Keywords
* Artificial intelligence * Deep learning * Gan * Generative adversarial network * Image inpainting * Super resolution
