On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications
October 27, 2017 Β· Declared Dead Β· π International Conference on Pattern Recognition Applications and Methods
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Niels Chr. Overgaard
arXiv ID
1710.10985
Category
eess.IV: Image & Video Processing
Cross-listed
cs.CV
Citations
3
Venue
International Conference on Pattern Recognition Applications and Methods
Last Checked
5 months ago
Abstract
A new proof of the equivalence of the Taut String Algorithm and the one-dimensional Rudin-Osher-Fatemi model is presented. Based on duality and the projection theorem in Hilbert space, the proof is strictly elementary. Existence and uniqueness of solutions to both denoising models follow as by-products. The standard convergence properties of the denoised signal, as the regularizing parameter tends to zero, are recalled and efficient proofs provided. Moreover, a new and fundamental bound on the denoised signal is derived. This bound implies, among other things, the strong convergence (in the space of functions of bounded variation) of the denoised signal to the insignal as the regularization parameter vanishes. The methods developed in the paper can be modified to cover other interesting applications such as isotonic regression.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Image & Video Processing
R.I.P.
π»
Ghosted
π
π
The Cartographer
Deep Learning for Hyperspectral Image Classification: An Overview
R.I.P.
π»
Ghosted
U-Net and its variants for medical image segmentation: theory and applications
R.I.P.
π»
Ghosted
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
R.I.P.
π
404 Not Found
Lightweight Image Super-Resolution with Information Multi-distillation Network
R.I.P.
π»
Ghosted
Deep Learning on Image Denoising: An overview
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted