Summary of Mgan-crcm: a Novel Multiple Generative Adversarial Network and Coarse-refinement Based Cognizant Method For Image Inpainting, by Nafiz Al Asad et al.
MGAN-CRCM: A Novel Multiple Generative Adversarial Network and Coarse-Refinement Based Cognizant Method for Image Inpainting
by Nafiz Al Asad, Md. Appel Mahmud Pranto, Shbiruzzaman Shiam, Musaddeq Mahmud Akand, Mohammad Abu Yousuf, Khondokar Fida Hasan, Mohammad Ali Moni
First submitted to arxiv on: 25 Dec 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 The paper proposes a novel architecture for image inpainting using Generative Adversarial Networks (GANs) and Residual Networks (ResNet). The model integrates three components: Transpose Convolution-based GAN for guided and blind inpainting, Fast ResNet-Convolutional Neural Network (FR-CNN) for object removal, and Co-Modulation GAN (Co-Mod GAN) for refinement. The paper evaluates the model’s performance on benchmark datasets, achieving accuracies of 96.59% on Image-Net, 96.70% on Places2, and 96.16% on CelebA, outperforming existing methods in both qualitative and quantitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to fix broken or missing parts in images using special types of artificial intelligence called Generative Adversarial Networks (GANs) and Residual Networks (ResNet). The method combines three parts: one that helps with guided and blind image repair, another that removes objects, and the third that refines the result. The new way is tested on several famous image datasets and does better than other methods in fixing images. |
Keywords
» Artificial intelligence » Cnn » Gan » Image inpainting » Neural network » Resnet
