Add to ORKGIn many practical applications, it turned out to be efficient to assume that the signal or an image is sparse, i.e., that when we decompose it into appropriate basic functions (e.g., sinusoids or wavelets), most of the coefficients in this decomposition will be zeros. At present, the empirical efficiency of sparsity-based techniques remains somewhat a mystery. In this paper, we show that fuzzy-related techniques can explain this empirical efficiency. A similar explanation can be obtained by using probabilistic techniques; this fact increases our confidence that our explanation is correct
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
Sparse coding is a proven principle for learning compact representations of images. However, sparse ...
International audienceNatural images follow statistics inherited by the structure of our physical (v...
One of the main techniques used to de-noise and de-blur signals and images is regularization, which ...
In many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when ...
Several decades ago, traditional neural networks were the most efficient machine learning technique....
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
In signal and image processing, it is often beneficial to use semi-heuristic Lp-methods, i.e., metho...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
AbstractThe spatial receptive fields of simple cells in mammalian striate cortex have been reasonabl...
Image processing problems have always been challenging due to the complexity of the signal. These pr...
Diese Dissertation widmet sich nichtkonvexen und nichtglatten Minimierungsproblemen in der auf dünne...
Data analysis is only interesting when the data has structure — there’s not much you can do with ran...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
Sparse coding is a proven principle for learning compact representations of images. However, sparse ...
International audienceNatural images follow statistics inherited by the structure of our physical (v...
One of the main techniques used to de-noise and de-blur signals and images is regularization, which ...
In many situations, a solution to a practical problem is sparse, i.e., corresponds to the case when ...
Several decades ago, traditional neural networks were the most efficient machine learning technique....
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
The real-world data nowadays is usually in high dimension. For example, one data image can be repres...
In signal and image processing, it is often beneficial to use semi-heuristic Lp-methods, i.e., metho...
Today, sparsity techniques have been widely used to address practical problems in the fields of medi...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
AbstractThe spatial receptive fields of simple cells in mammalian striate cortex have been reasonabl...
Image processing problems have always been challenging due to the complexity of the signal. These pr...
Diese Dissertation widmet sich nichtkonvexen und nichtglatten Minimierungsproblemen in der auf dünne...
Data analysis is only interesting when the data has structure — there’s not much you can do with ran...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
Sparse coding is a proven principle for learning compact representations of images. However, sparse ...
International audienceNatural images follow statistics inherited by the structure of our physical (v...