We propose in this paper to unify two different ap-
proaches to image restoration: On the one hand, learning a
basis set (dictionary) adapted to sparse signal descriptions
has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data. Empirical studies show that mapping the data...
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower di...
A sparse representation based method is proposed for text detection from scene images. We start with edge information extracted using Canny operator and then group these edge poin...