A Sparse Representation Image Denoising Method Based on Orthogonal Matching Pursuit
Abstract: Image denoising is
an important research aspect in the field of digital image processing, andsparse
representation theory is also one of the research focuses in recent years. The
sparse representation of the image can better extract the nature of the image,
and use a way as concise as possible to express the image. In image denoising
based on sparse representation, the useful information of the image possess
certain structural features, which match the atom structure. However, noise
does not possess such property, therefore, sparse representation can effectively
separate the useful information from noise to achieve the purpose of denoising.
Aiming at image denoising problem of low signal-to-noise ratio (SNR) image,
combined with Orthogonal Matching Pursuit and sparse representation theory,
this paper puts forward an image denoising method. The experiment shows that
compared with the traditional image denoising based on Symlets, image denoising
based on Contourlettransform, this method can delete noise in low SNR image and
keep the useful information in the original image more efficiently.
Author: Xiaojun Yu, Defa Hu
Journal Code: jptkomputergg150144
