Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Long-Range Random Field and Its Application to Image Denoising
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
A novel PDE based image restoration: Convection-diffusion equation for image denoising
Journal of Computational and Applied Mathematics
A moment-based nonlocal-means algorithm for image denoising
Information Processing Letters
Removal of correlated noise by modeling the signal of interest in the wavelet domain
IEEE Transactions on Image Processing
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Edge structure preserving image denoising
Signal Processing
Switching-based filter based on Dempster's combination rule for image processing
Information Sciences: an International Journal
The Shiftable Complex Directional Pyramid—Part I: Theoretical Aspects
IEEE Transactions on Signal Processing - Part I
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
IEEE Transactions on Image Processing
Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids
IEEE Transactions on Image Processing
Efficient Nonlocal Means for Denoising of Textural Patterns
IEEE Transactions on Image Processing
Nonlinear Regularized Reaction-Diffusion Filters for Denoising of Images With Textures
IEEE Transactions on Image Processing
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures
IEEE Transactions on Image Processing
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It is a challenging work to design an edge structure preserving image denoising. In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image denoising. Its strength relies on providing a simple and, yet, very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation, and spatial location. Combining with Bayes least squares estimator, we describe a method for removing noise from digital images, based on orientation-adapted GSM with nonoriented component (OAGSM/NC) in shiftable complex directional pyramid (PDTDFB) domain in this paper, which can be seen a modified version of the BLS-GSM. By introducing a coarser adaptation level, we model the distribution of PDTDFB coefficients with OAGSM/NC. The statistical model is then used to obtain the denoised coefficients from the noisy image decomposition by Bayes least squares estimator. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.