An Augmented Lagrangian Method for Total Variation Video Restoration

By Stanley H. Chan, Ramsin Khoshabeh, Kristofor B. Gibson, Philip E. Gill, Truong Q. Nguyen. IEEE Transactions on Image Processing, 2011

474 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7349347b74af738b0bde62d11f582e5aaaea9548

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. Ming-Hsuan Yang
2. Luc Van Gool
3. Chen Change Loy
4. Dacheng Tao
5. Wenhan Yang
6. Jiaying Liu
7. Ling Shao
8. Haibin Ling

Citations in the corpus

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, methodology): “A candidate tumor mask is defined by thresholding (50% percentile) a denoised [99] -weighted FLAIR image.”
• LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, background): “leads (8) to a classic `2 loss total variation minimization problem [33].”
• Deep Retinex Decomposition for Low-Light Enhancement (2018, methodology): “Total variation minimization (TV) [2], which minimizes the gradient of the whole image, is often used as smoothness prior for various image restoration tasks.”