Image inpainting through neural networks hallucinations

By Alhussein Fawzi, Horst Samulowitz, Deepak S. Turaga, Pascal Frossard. 2016

31 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yizhou Yu
2. Liang Lin
3. Guanbin Li
4. Huchuan Lu
5. Ming-Ming Cheng
6. Ali Borji
7. Pascale Fung
8. Haofeng Li

Citations in the corpus

• Survey of Hallucination in Natural Language Generation (2022, background): “1The term “hallucination” first appeared in Computer Vision (CV) in Baker and Kanade [5] and carried more positive meanings, such as superresolution [5, 112], image inpainting [48], and image synthesizing [226].”
• Context-Aware Semantic Inpainting (2017, background): “Deep convolutional neural networks are capable of learning powerful image representations and have been applied to inpainting [3], [4] with varying degrees of success.”
• PEPSI : Fast Image Inpainting With Parallel Decoding Network (2019, methodology): “Image inpainting techniques have been widely re-searched [1–3, 5, 6, 10, 13, 16, 17, 19, 21, 25, 26, 28] for removing an unwanted object or synthesizing missing parts of an image in various applications such as photo editing, image-based rendering, and computational photography [15, 20, 28].”