Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising
Journal of Mathematical Imaging and Vision
Noisy Image Decomposition: A New Structure, Texture and Noise Model Based on Local Adaptivity
Journal of Mathematical Imaging and Vision
Automatic joint classification and segmentation of whole cell 3D images
Pattern Recognition
Variational denoising of partly textured images
Journal of Visual Communication and Image Representation
Locally Adaptive Total Variation Regularization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Simultaneous MAP-based video denoising and rate-distortion optimized video encoding
IEEE Transactions on Circuits and Systems for Video Technology
A new denoising system for SONAR images
Journal on Image and Video Processing
Uniform and textured regions separation in natural images towards MPM adaptive denoising
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Scale selection for anisotropic diffusion filter by Markov random field model
Pattern Recognition
Comments on "Staircase effect alleviation by coupling gradient fidelity term"
Image and Vision Computing
Multiplicative Noise Removal with Spatially Varying Regularization Parameters
SIAM Journal on Imaging Sciences
Adaptive Fractional-order Multi-scale Method for Image Denoising
Journal of Mathematical Imaging and Vision
A relaxed split bregman iteration for total variation regularized image denoising
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Lagrangian multipliers and split Bregman methods for minimization problems constrained on Sn-1
Journal of Visual Communication and Image Representation
Journal of Mathematical Imaging and Vision
Computers & Mathematics with Applications
Image Restoration via Tight Frame Regularization and Local Constraints
Journal of Scientific Computing
On a System of Adaptive Coupled PDEs for Image Restoration
Journal of Mathematical Imaging and Vision
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Denoising algorithms based on gradient dependent regularizers, such as nonlinear diffusion processes and total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. We propose a mechanism that better preserves fine scale features in such denoising processes. A basic pyramidal structure-texture decomposition of images is presented and analyzed. A first level of this pyramid is used to isolate the noise and the relevant texture components in order to compute spatially varying constraints based on local variance measures. A variational formulation with a spatially varying fidelity term controls the extent of denoising over image regions. Our results show visual improvement as well as an increase in the signal-to-noise ratio over scalar fidelity term processes. This type of processing can be used for a variety of tasks in partial differential equation-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint