Y Lou, X Zhang, S Osher, A Bertozzi, Image recovery via nonlocal operators. J. Sci. Comput. 42(2), 185–197 (2010). MATH MathSciNetYifei Lou , Xiaoqun Zhang , Stanley Osher , Andrea Bertozzi, Image Recovery via Nonlocal Operators, Journal of Scientific Computing, v.42 n.2, p....
Buades A, Coll B, Morel JM: A non-local algorithm for image denoising. IEEE International Conference on Computer Vision and pattern Recognition, San Diego 60-65. June 2005 Lou Y, Zhang X, Osher S, Bertozzi A: Image recovery via nonlocal operators. J. Sci. Comput 2010, 42: 185-197. ...
Image Recovery via Nonlocal Operators This paper considers two nonlocal regularizations for image recovery, which exploit the spatial interactions in images. We get superior results using prepr... Y Lou,X Zhang,S Osher,... - 《Journal of Scientific Computing》 被引量: 459发表: 2010年 Image...
The non-noisy image will be superresolved by NARM (Nonlocal Autoregressive Modeling). NARM-based image Super-resolution method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image superresolution. A novel image prior model, namely non-...
It has been proved that the geometric information in images produce most stimulus of human eyes, which is important for the image recovery. However, the local geometric structure in images are too diverse to be accurately captured. Recent decade has witnessed a flourish of biological inspired algor...
Analysis of focus measure operators for shape-from-focus Pattern Recognit. (2013) A.S.Maliket al. Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3d shape recovery Pattern Recognit. ...
In ref. [73], a hybrid noise removal algorithm based on low-rank matrix recovery was proposed. Dong et al. [74] proposed a low-rank method based on SVD to model the sparse representation of non-locally similar image patches. In this method, singular value iteration contraction in the ...
In classical multi-view recovery problem, the point spread function (PSF) kernels are often low-pass operators [28]. Many recent researchers have designed various priors used for multi-view deconvolution, such as the gradient prior, the hyper-Laplacian prior, the Gaussian prior [29–31] and ...
In classical multi-view recovery problem, the point spread function (PSF) kernels are often low-pass operators [28]. Many recent researchers have designed various priors used for multi-view deconvolution, such as the gradient prior, the hyper-Laplacian prior, the Gaussian prior [29–31] and ...
recovery and obtained good results. In ref. [73], a hybrid noise removal algorithm based on low-rank matrix recovery was proposed. Dong et al. [74] proposed a low-rank method based on SVD to model the sparse representation of non-locally similar image patches. In this method, singular ...