Centralized sparse representation for image restoration

By Weisheng Dong, Lei Zhang, Guangming Shi. 2011

268 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Countering Adversarial Images using Input Transformations (2018, positive): “Therefore, we surmise that total variation minimization, image quilting, and related methods (Dong et al., 2011) are stronger defenses than deterministic denoising procedures such as bit-depth reduction, JPEG compression, or non-local means (Buades, 2005).”
Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis (2012, background): “Recently, sparse coding (or sparse representation) and dictionary learning have proven to be very effective in image reconstruction [20, 9, 6, 5], while the dictionary plays an important role to successfully accomplish such tasks.”
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, methodology): “In [27], a centralized sparse coding model was also proposed to exploit the image nonlocal redundancy.”
Image Super-Resolution With Sparse Neighbor Embedding (2012, methodology): “neighbors ( -NN) learning methods [9]–[14] and sparse coding methods [15]–[20].”
Weighted Nuclear Norm Minimization with Application to Image Denoising (2014, methodology): “The NSS based image denoising algorithms such as BM3D [7], LSSC [22] and NCSR [10] have achieved state-of-the-art denoising results.”
Sparse Modeling for Image and Vision Processing (2014, methodology): “…inpainting and demosaicking [Mairal et al., 2008c, 2009], super-resolution and deblurring [Yang et al., 2010a, 2012a, Couzinie-Devy et al., 2011, Dong et al., 2011b, Zeyde et al., 2012, Wang et al., 2012], face compression [Bryt and Elad, 2008], or for inverting non-linear local transformations…”
Joint Bi-layer Optimization for Single-Image Rain Streak Removal (2017, methodology): “This prior integrates both local and nonlocal sparsity constraints and adapts the CSR for de-raining by constructing a guidance image with the window inherent variation metric [30].”
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision (2016, methodology): “2009), NCSR (Dong et al. 2011) and SAIST (Dong et al.”
A Survey of Sparse Representation: Algorithms and Applications (2015, methodology): “The mai n procedures of the CSR algorithm are summarized in Algorithm 16 and readers may refer to literature [181] for more details .”
Nonlocally Centralized Sparse Representation for Image Restoration (2013, methodology): “Due to the ill-posed nature of IR, the regularization-based techniques have been widely used by regularizing the solution spaces [5]–[9], [12], [22].”
Deep Networks for Image Super-Resolution with Sparse Prior (2015, unknown): “We also compare CSCN with other sparse coding extensions [22, 10, 38], and consider the blurring effect introduced in downscaling.”