On Image Denoising Method Based on Sparse Representation

An important way for people to obtain information is vision. However, during the collection and transmission of digital images, there is often some noise that is not good for people to get information quickly and correctly. Therefore, before the image processing analysis, the image denoising pretreatment is particularly important. However, the traditional denoising method will lose the details of the image and the result of denoising is unsatisfactory. In recent years, with the continuous improvement of sparse representation theory, it has also made great progress in image processing and has been well applied in image denoising, which has become one of the most effective methods in current image denoising methods. Using sparse coding decomposes the image on sparse domain, and the handling of the sparse coefficient by setting threshold. And based on a sparse representation of an overly-complete dictionary, an image is represented by a linear combination of atoms in the dictionary. Its adaptability also brings better denoising effect.