Image Denoising Based on K-means Singular Value Decomposition
Abstract: The image is usually
polluted by noises in its acquisition and transmission and noises are of great
importance in the image quality, therefore, image de-noising has become a
significant technique in image analysis and processing. In the image de-noising
based on sparse representation, one of the hot spots in recent years, the
useful image information has certain structural features, which coincide with
the atomic structure while noises don’t have such features, therefore, sparse
representation can separate the useful information from the noises effectively
so as to achieve the purpose of de-noising. In view of the above-mentioned
theoretical basis, this paper proposes an image de-noising algorithm of sparse
representation based on K-means Singular Value Decomposition (K-SVD). This
method can integrate the construction and optimization of over-complete
dictionary, train the atom dictionary with the image samples to be decomposed
and effectively build the atom dictionary that reflects various image features
to enhance the de-noising performance of the algorithm in this paper. Through
simulation analysis, this method can conduct noise filtration on the image with
different noise densities and its de-noising effect is also better than other
methods.
Author: Jian Ren, Hua Lu,
Xiliang Zeng
Journal Code: jptkomputergg150140
