@inproceedings{82810491f353458fb2f47c2991a59a9e,
title = "Compressive sampling recovery for natural images",
abstract = "Compressive sampling (CS) is a novel data collection and coding theory which allows us to recover sparse or compressible signals from a small set of measurements. This paper presents a new model for natural image recovery, in which the smooth l0 norm and the approximate total-variation (TV) norm are adopted simultaneously. By using one-order gradient decrease, the speed of algorithm for this new model can be guaranteed. Experimental results demonstrate that the principle of the model is correct and the performance is as good as that based on TV model. The computing speed of the proposed method is two orders of magnitude faster than that of interior point method and two times faster than that of the Nesta optimization based on TV model.",
keywords = "Compressive sampling, Image recovery, Smooth l norm, TV norm",
author = "Fei Shang and Huiqian Du and Yunde Jia",
year = "2010",
doi = "10.1109/ICPR.2010.540",
language = "English",
isbn = "9780769541099",
series = "Proceedings - International Conference on Pattern Recognition",
pages = "2206--2209",
booktitle = "Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010",
note = "2010 20th International Conference on Pattern Recognition, ICPR 2010 ; Conference date: 23-08-2010 Through 26-08-2010",
}