Image Compression With Edge-Based Inpainting

By Dong Liu, Xiaoyan Sun, Feng Wu, Shipeng Li, Ya-Qin Zhang. 2007

220 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. Tony Joseph
2. Zongyu Guo
3. Tao Yu
4. Faisal Z. Qureshi
5. Eric Ng
6. Jiale Chen
7. Zhibo Chen
8. Mehran Ebrahimi

Citations in the corpus

• EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning (2019, methodology): “Diffusion-based methods propagate background data into the missing region by following a diffusive process typically modeled using differential operators [4, 14, 27, 2].”
• EdgeConnect: Structure Guided Image Inpainting using Edge Prediction (2019, methodology): “Diffusion-based methods propagate background data into the missing region by numerically solving corresponding partial differential equations (PDEs) that model desired behaviors [4, 15, 30, 2].”
• Progressive Image Inpainting with Full-Resolution Residual Network (2019, methodology): “Traditional diffusion-based methods [1, 4, 7, 15, 17] are able to utilize texture or structure information in known areas to reconstruct missing areas.”