New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction

By E.J. Candes, Franck Guo. Signal Processing, 2002

316 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d93ecda52614f0c6302cc7c796b09b6c08dd4c98

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Real-Time Convex Optimization in Signal Processing (2010, methodology): “Similar l1-techniques are widely used in total variation noise removal in image processing [42], [43], [44].”
Compressed Sensing MRI (2008, methodology): “Even if another sparsifying transform is intended, it is often useful to include a TV penalty as well [49]–[51] Such a combined objective seeks image sparsity both in the transform domain and the finite-differences domain, simultaneously.”
Continuous curvelet transform (2003, methodology): “In accord with the use of the terminology curvelet to denote families exhibiting such parabolic scaling [4, 2, 5, 3], we call this system of analyzing elements curvelets.”
New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities (2004, methodology): “In particular, we would like to point out applications in image processing [10, 33, 34], statistical estimation [8], and possibly partial differential equations and scientiﬁc computing.”
New tight frames of curvelets and optimal representations of objects with C² singularities (2002, methodology): “We would like to remark that the construction presented here was rapidly introduced by Candès and Guo in [9].”
Templates for convex cone problems with applications to sparse signal recovery (2010, background): “4) is to look for a sparse object in a transformed domain while reducing artifacts due to sparsity constraints alone, such as Gibbs oscillations, by means of the total-variation norm [12,15,59].”
The Undecimated Wavelet Decomposition and its Reconstruction (2007, methodology): “This has motivated the recent development of iterative techniques combining at the same time a multiscale method and a penalization term such as the Total Variation (TV) [12]–[14] or the norm of coefficients in the wavelet or curvelet decomposition [15].”
Continuous curvelet transform: II. Discretization and frames (2005, background): “Section 4 samples the CCT to produce a frame and explains that this is a complexified version of the discrete curvelet frame in [1] [3] [2].”
Comparisons of wavelets, contourlets and curvelets in seismic denoising (2009, unknown): “Alternatively, curvelet transform (Candès and Donoho, 2000; Candès and Guo, 2002; Candès and Donoho, 2004; Candès et al., 2006) is a new geometric multiscale transform.”
Practical Signal Recovery from Random Projections (2005, background): “We limited our exposition to wavelet bases although it is clear that other representations might offer enhanced sparsity [12], [17].”
Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer (2014, methodology): “The CT was developed by Candes and Donoho 37 to overcome the missing ability of the conventional wavelet transform to highlight lines and edges.”
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing (2003, methodology): “Other work for restoration of textured images by total variation minimization in a wavelet framework is by Malgouyres [15, 16], Candés and Guo [29].”
The curvelet representation of wave propagators is optimally sparse (2004, background): “Curvelets are a recently developed multiscale system [10, 7] in which the elements are highly anisotropic at fine scales, with effective support shaped according to the parabolic scaling principle width ≈ length at fine scales.”