Yes, sparse-coding-based methods are still important in the field of super-resolution, although their significance has evolved over time. Sparse coding has been a crucial component in the development of super-resolution algorithms, and it provided a solid foundation for later methods. It essentially...
For datasets with low dimensionality and low difficulty level, traditional methods outperform sparse coding-based outlier detection (SCOD) algorithms. But the trend reverses as the dimensionality or difficulty level increases. A threshold emerges as the point of intersection of the trends for SCOD and...
讲座论坛简介 In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. In this talk, I will introduce some sparse coding based dictionary learning methods in image processing. An alternating numerical scheme for solving the resulting...
- 《Computer Methods & Programs in Biomedicine》 被引量: 19发表: 2013年 Online-Learning Structural Appearance Model for Robust Visual Tracking In this paper, we propose a robust tracking method with an online-learning structural appearance model based on local sparse coding and online metric ......
Existing methods largely use various types of sensors to collect useful parameters and then train a machine learning model to diagnose damage level and location, in which a large amount of training data are needed for the model training, while the labeled data are rare in the real world. To ...
The existing SC methods used in ScSPM fail to consider either the color information, or the inherent correlation among different color channels in a color image. Recently, the joint sparsity model (JSM) for SC has achieved great success in image processing and analysis, e.g., image fusion [...
Existing methods largely use various types of sensors to collect useful parameters and then train a machine learning model to diagnose damage level and location, in which a large amount of training data are needed for the model training, while the labeled data are rare in the real world. To ...
Learning-Based super-resolution methods usually select several objects with similar features from some examples according to the low-resolution image, then estimate super-resolution result using optimization algorithm. But the result is usually limited b
Super-Resolution is one of the methods used to enhance image resolution.Up until now, an Learning-Based Super-Resolution method that utilizes high resolution image and low resolution image pairings in order to compensate high frequency component has been proposed.In this conventional method, similarity...
Sparse Matrix Methods:稀疏矩阵的方法 MATERIAL AND CODING物料编码 A review of automated feature recognition with rule-based pattern recognition a general framework for image fusion based on multi-scale transform and sparse representation:基于多尺度变换和稀疏表示的图像融合总体框架 Arabic Font Recognition Ba...