A Review of Image Denoising Algorithms, with a New One

@article{Buades2005ARO,
  title={A Review of Image Denoising Algorithms, with a New One},
  author={Antoni Buades and Bartomeu Coll and Jean-Michel Morel},
  journal={Multiscale Model. Simul.},
  year={2005},
  volume={4},
  pages={490-530}
}
The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a… 
Image Denoising Methods. A New Nonlocal Principle
TLDR
A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image are defined.
Self-similarity-based image denoising
TLDR
A general mathematical and experimental methodology to compare and classify classical image denoising algorithms and to describe the nonlocal means (NL-means) algorithm introduced in 2005 and its more recent extensions is defined.
Boosting of Image Denoising Algorithms
TLDR
This paper introduces an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance.
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms
TLDR
This paper starts with a statistical analysis on non-local similarity and its effectiveness under various noise levels, followed by a theoretical comparison of different state-of-the-art image denoising algorithms.
A Nonlocal Bayesian Image Denoising Algorithm
TLDR
A simple patch-based Bayesian method is proposed, which on the one hand keeps most interesting features of former methopping methods and on the other hand unites the transform thresholding method and a Markovian Bayesian estimation.
A fast non-local image denoising algorithm
TLDR
Compared to the original algorithm, the proposed method produces images with increased PSNR and better visual performance in less computation time, and outperforms state-of-the-art wavelet denoising techniques in both visual quality and PSNR values for images containing a lot of repetitive structures such as textures.
Sequence-to-Sequence Similarity-Based Filter for Image Denoising
TLDR
A new concept of sequence-to-sequence similarity (SSS) is presented, an efficient method to evaluate the content similarity for images, especially for edge information, and a new SSS-based filter is introduced, which utilizes the edge information in the corrupted image to address image denoising problems.
Advances in Image Denoising
TLDR
An image denoising algorithm is described that seems to perform better in quality and PSNR than any other based on the right combination of bothDenoising principles, and a new metric is proposed to improve on this matter.
Image Denoising Using Hybrid Thresholding, DWT and Adaptive Intensity Transformations
TLDR
A new method for the removal of Gaussian noise is proposed, majorly based on a hybrid of Neigh and Bayes shrink, and Discrete Wavelet Transform, which is a kind of extension of previous techniques.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 58 REFERENCES
Image denoising with unsupervised, information-theoretic, adaptive filtering
TLDR
A novel unsupervised, information-theoretic, adaptive fil­ ter (UINTA) that improves the predictability of pixel intensities from their neighborhoods by decreasing the joint entropy between them and can thereby reduce noise in a wide spectrum of images and applications.
Restoration of wavelet coefficients by minimizing a specially designed objective function
TLDR
This work designs a specialized (non-smooth) objective function allowing all these coefficients to be selectively restored, without modifying the other coefficients, and proposes an approximation of this method which is accurate enough and very fast.
Noise selection approach to image restoration
TLDR
This paper deals with a restoration (both denoising and deblurring) method that has the significant advantage to allow the use of several bases in such a way that the authors select what is considered as information by a basis or another basis oranother basis, and so on for as many bases as they want.
Transform domain image restoration methods: review, comparison, and interpretation
TLDR
The paper provides results of extensive experimental comparisons of image restoration capabilities of the methods and demonstrates that they can naturally be interpreted in a unified way as different implementations of signal sub-band decomposition with uniform (in SWTD filters) or logarithmic (for WL-methods) arrangement of signalSub-bands and element-wise processing decomposed components.
Image Decomposition and Restoration Using Total Variation Minimization and the H1
TLDR
A new model for image restoration and image decomposition into cartoon and texture is proposed, based on the total variation minimization of Rudin, Osher, and Fatemi, and on oscillatory functions, which follows results of Meyer.
IMAGE DECOMPOSITION AND RESTORATION USING TOTAL VARIATION MINIMIZATION AND THE H−1 NORM∗
TLDR
A new model for image restoration and image decomposition into cartoon and texture is proposed, based on the total variation minimization of Rudin, Osher, and Fatemi, and on oscillatory functions, which follows results of Meyer.
The curvelet transform for image denoising
We describe approximate digital implementations of two new mathematical transforms, namely, the ridgelet transform and the curvelet transform. Our implementations offer exact reconstruction,
Image recovery via total variation minimization and related problems
TLDR
A variant of the original TV minimization problem that handles correctly some situations where TV fails is proposed, and an alternative approach whose purpose is to handle the minimization of the minimum of several convex functionals is proposed.
SUSAN—A New Approach to Low Level Image Processing
TLDR
This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction and the resulting methods are accurate, noise resistant and fast.
...
1
2
3
4
5
...