Sparse representation for image denoising

1. What is sparse representation in image processing?
2. What is meant by sparse representation?
3. Why and when do we use sparse representation?
4. What is sparse representation classification?

What is sparse representation in image processing?

Sparse representation theory puts forward an emerging, highly effective, and universal such model. Its core idea is the description of the data as a linear combination of few building blocks - atoms - taken from a pre-defined dictionary of such fundamental elements.

What is meant by sparse representation?

Sparse representations intend to represent signals with as few as possible significant coefficients. This is important for many applications, like for instance compression. When using wavelets it is frequently noticed that a great compression rate can be obtained, with almost unnoticeable loss of information.

Why and when do we use sparse representation?

Sparse representation attracts great attention as it can significantly save computing resources and find the characteristics of data in a low-dimensional space. Thus, it can be widely applied in engineering fields such as dictionary learning, signal reconstruction, image clustering, feature selection, and extraction.

What is sparse representation classification?

Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels.