Image Inpainting : Overview and Recent Advances
By Christine M. Guillemot, O. Le Meur. IEEE Signal Processing Magazine, 2014
604 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/436c9b823db44be88d6d33e2310952c0db78ddc7
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
- Alhussein Fawzi
- Liangpei Zhang
- Pascal Frossard
- Huanfeng Shen
- Errui Ding
- Zhaoxiang Zhang
- Amit K. Roy-Chowdhury
- Matthew J. Guzdial
Citations in the corpus
- Image inpainting through neural networks hallucinations (2016, background): “This fundamental problem has received significant attention from the image processing and computer vision communities throughout the years, and led to key advances in the field (see [6] and references therein).”
- Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (2019, background): “The paper [35] highlights the recent advances in image manipulation and also discusses the process of restoring missing or damaged areas in an image.”
- Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, background): “It should be noted that because of the lack of other reference data to judge the recovery fidelity, the goal of spatial-based reconstruction is that the recovered region shows a smooth transition at the junctions, and is as physically plausible as possible, without unconnected edges, blurring, or in”
- Image Inpainting by End-to-End Cascaded Refinement With Mask Awareness (2021, background): “More detailed review of conventional algorithms can be find from [31], [32].”
- Procedural Content Generation via Machine Learning (PCGML) (2017, background): “Within the image domain, we have seen work on image inpainting, where a neural network is trained to complete images where parts are missing [20].”
- Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation (2015, background): “Image interpolation or inpainting is also a widely researched problem [29].”