Fundamentals of digital image processing
Fundamentals of digital image processing
Robust model-based motion tracking through the integration of search and estimation
International Journal of Computer Vision
Learning internal representations
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The canonical distortion measure in feature space and 1-NN classification
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Image quilting for texture synthesis and transfer
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Real-time texture synthesis by patch-based sampling
ACM Transactions on Graphics (TOG)
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation
International Journal of Computer Vision
Non-local Regularization of Inverse Problems
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
IEEE Transactions on Image Processing
Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
An intensity similarity measure in low-light conditions
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Non-local Methods with Shape-Adaptive Patches (NLM-SAP)
Journal of Mathematical Imaging and Vision
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of GLR and invariant detectors under structured clutter covariance
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising
IEEE Transactions on Image Processing
A new similarity measure for non-local means filtering of MRI images
Journal of Visual Communication and Image Representation
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Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison.A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning.By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications.