An introduction to Total Variation for Image Analysis

518 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Variable Selection (2019, methodology): “As for the features Jacobian, in the eye-in-hand conﬁguration, it can be decomposed as [2] J s D L s c V n J . q / (3) where –”
A General Framework for a Class of First Order Primal-Dual Algorithms for Convex Optimization in Imaging Science (2010, background): “In recent preprints [11] [12] that appeared during the review process, this convergence argument has been generalized and gives a stronger statement of the convergence of the modified PDHG algorithm for the same range of fixed parameters.”
A Direct Algorithm for 1-D Total Variation Denoising (2013, background): “…functions from R N to R ∪ { + ∞} [9]. be Many problems in signal and image processing can formulated as ﬁnding a minimizer x ∈ R N of the sum of functions F i ∈ Γ 0 ( R N ) , where each F i is introduced to enforce some constraint or promote some property on the solution [6], [9], [16], [18].”
Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems (2018, unknown): “For such applications, the commonly used loss function is F (x) = 1 2 ‖Ax− b‖ 2 2 + λ ‖x‖TV [5].”