Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
High-Order Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Nonlinear Matrix Diffusion for Optic Flow Estimation
Proceedings of the 24th DAGM Symposium on Pattern Recognition
TV Based Image Restoration with Local Constraints
Journal of Scientific Computing
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
Image Decomposition into a Bounded Variation Component and an Oscillating Component
Journal of Mathematical Imaging and Vision
Image restoration combining a total variational filter and a fourth-order filter
Journal of Visual Communication and Image Representation
A TV Based Restoration Model with Local Constraints
Journal of Scientific Computing
Mumford-Shah regularizer with spatial coherence
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Noise removal using smoothed normals and surface fitting
IEEE Transactions on Image Processing
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
Variational denoising of partly textured images by spatially varying constraints
IEEE Transactions on Image Processing
Computers & Mathematics with Applications
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
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The Rudin-Osher-Fatemi model is a widely used variational denoising algorithm which favors piecewise constant solutions. Although edge sharpness and location are well preserved, some local features such as textures and small details are often diminished with noise simultaneously. This paper aims to better preserve these local features using a similar variational framework. We introduce a texture detecting function according to the derivatives of the noisy textured image. Then this function is used to construct a spatially adaptive fidelity term, which adjusts the denoising extent in terms of the local features. Numerical results show that our method is superior to the Rudin-Osher-Fatemi model in both signal-to-noise ratio and visual quality. Moreover, part of our results are also compared with other state-of-the-art methods including a variational method and a non local means filter. The comparison shows that our method is competitive with these two methods in restoration quality but is much faster.