Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
A non-parametric multi-scale statistical model for natural images
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental Limits of Reconstruction-Based Superresolution Algorithms under Local Translation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denoising based on the edge-process model
Signal Processing
Limits of Learning-Based Superresolution Algorithms
International Journal of Computer Vision
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
Foundations and Trends in Signal Processing
Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.
Ziv-zakai bounds on image registration
IEEE Transactions on Signal Processing
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
A lower bound on the Bayesian MSE based on the optimal bias function
IEEE Transactions on Information Theory
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
All of Statistics: A Concise Course in Statistical Inference
All of Statistics: A Concise Course in Statistical Inference
MSE Bounds With Affine Bias Dominating the CramÉr–Rao Bound
IEEE Transactions on Signal Processing - Part II
Statistical modeling and conceptualization of visual patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental performance limits in image registration
IEEE Transactions on Image Processing
Statistical performance analysis of super-resolution
IEEE Transactions on Image Processing
Optimal Spatial Adaptation for Patch-Based Image Denoising
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Multiscale Hybrid Linear Models for Lossy Image Representation
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Bias modeling for image denoising
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
An MMSE approach to nonlocal image denoising: Theory and practical implementation
Journal of Visual Communication and Image Representation
Patch complexity, finite pixel correlations and optimal denoising
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
A stochastic image denoising algorithm using 3-D block filtering under a non-local means framework
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Analysis of classification accuracy for pre-filtered multichannel remote sensing data
Expert Systems with Applications: An International Journal
Perceptually optimized blind repair of natural images
Image Communication
Joint image denoising using adaptive principal component analysis and self-similarity
Information Sciences: an International Journal
A general non-local denoising model using multi-kernel-induced measures
Pattern Recognition
Conditional Toggle Mappings: Principles and Applications
Journal of Mathematical Imaging and Vision
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Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable. A pertinent question then to ask is whether there is a theoretical limit to denoising performance and, more importantly, are we there yet? As camera manufacturers continue to pack increasing numbers of pixels per unit area, an increase in noise sensitivity manifests itself in the form of a noisier image.We study the performance bounds for the image denoising problem. Ourwork in this paper estimates a lower bound on the mean squared error of the denoised result and compares the performance of current state-of-the-art denoising methods with this bound. We show that despite the phenomenal recent progress in the quality of denoising algorithms, some room for improvement still remains for a wide class of general images, and at certain signal-to-noise levels. Therefore, image denoising is not dead--yet.