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Recently, an Expected Patch Log Likelihood (EPLL) method is presented for image denoising, which can well restore details of natural images. However, the EPLL is viewed as a local method, and seldom takes into account the relationship among patches. In this paper, a non-local EPLL algorithm using eigenvectors of the graph Laplacian of patches is proposed to fully exploit such relationship. In detail,...
In this paper, a hybrid framework is proposed for image denoising, in which several state-of-the-art denoising methods are efficiently incorporated with a well trade-off by using the prior of patches. In detail, unlike modeling patches with the prior in existed denoising methods, the prior estimation here is presented only to detect the attributes of patches. Then, noisy patches are clustered into...
In this paper, a group sparse model using Eigenvectors of the Graph Laplacian (EGL) is proposed for image denoising. Unlike the heuristic setting for each image and for each noise deviation in the traditional denoising method via the EGL, in our group-sparse-based method, the used eigenvectors are adaptively selected with the error control. Sequentially, a modified group orthogonal matching pursuit...
Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit...
In this paper, a sparse approximation algorithm using eigenvectors of the graph Laplacian is proposed for image denoising, in which the eigenvectors of the graph Laplacian of images are incorporated in the sparse model as basis functions. Here, an eigenvector-based sparse approximation problem is presented under a set of residual error constraints. The corresponding relaxed iterative solution is also...
In this paper, an image denoising algorithm is proposed using a rotational dictionary learned from image patches. Since the traditional dictionary-learning-based methods seldom take into account the rotational invariance for the dictionary, an improved K-means singular value decomposition (K-SVD) algorithm is developed with the rotation of atoms. In our method, the rotational version of atoms is introduced...
Sparse representation theory has been well developed in recent years. In this paper, we consider an image denoising problem which can be efficiently solved under the framework of the sparse representation theory. The traditional image denoising methods based on the sparse representation seldom take into account the special structure of the data. As an attempt to overcome such problem, the Graph regularized...
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