Hirakawa et al., 2011 - Google Patents
Skellam shrinkage: Wavelet-based intensity estimation for inhomogeneous Poisson dataHirakawa et al., 2011
View PDF- Document ID
- 7756543317061404714
- Author
- Hirakawa K
- Wolfe P
- Publication year
- Publication venue
- IEEE Transactions on Information Theory
External Links
Snippet
The ubiquity of integrating detectors in imaging and other applications implies that a variety of real-world data are well modeled as Poisson random variables whose means are in turn proportional to an underlying vector-valued signal of interest. In this article, we first show …
- 230000000875 corresponding 0 abstract description 15
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/624—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a separation criterion, e.g. independent component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00496—Recognising patterns in signals and combinations thereof
- G06K9/00503—Preprocessing, e.g. filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hirakawa et al. | Skellam shrinkage: Wavelet-based intensity estimation for inhomogeneous Poisson data | |
Antoniadis et al. | Regularization of wavelet approximations | |
Chang et al. | Spatially adaptive wavelet thresholding with context modeling for image denoising | |
Selesnick et al. | Signal restoration with overcomplete wavelet transforms: Comparison of analysis and synthesis priors | |
Remenyi et al. | Image denoising with 2D scale-mixing complex wavelet transforms | |
Choi et al. | Multiple wavelet basis image denoising using Besov ball projections | |
Shen et al. | Image denoising using a tight frame | |
Gai et al. | Denoising color images by reduced quaternion matrix singular value decomposition | |
Ali et al. | Proposed hybrid method for wavelet shrinkage with robust multiple linear regression model: With simulation study | |
Grigoryan et al. | Optimal Wiener and homomorphic filtration | |
Korki et al. | Iterative Bayesian reconstruction of non-IID block-sparse signals | |
Firoiu et al. | Image denoising using a new implementation of the hyperanalytic wavelet transform | |
Hughes et al. | Bayesian learning of sparse multiscale image representations | |
Zhang et al. | Variational Bayesian image restoration with group-sparse modeling of wavelet coefficients | |
Huang et al. | Multiplicative noise removal based on unbiased box-cox transformation | |
Sasirekha et al. | A novel wavelet based thresholding for denoising fingerprint image | |
Arico et al. | Spectral analysis of the anti-reflective algebra | |
Du | Image denoising algorithm based on nonlocal regularization sparse representation | |
Olhede | Hyperanalytic denoising | |
Zhang | The SURE-LET approach using hybrid thresholding function for image denoising | |
Mahajan et al. | Analysis of blind separation of noisy mixed images based on wavelet thresholding and independent component analysis | |
Sahu et al. | Image denoising using principal component analysis in wavelet domain and total variation regularization in spatial domain | |
Eslahi et al. | Improved sparse signal recovery via adaptive correlated noise model | |
Sheikh et al. | Compression and denoising of speech transmission using Daubechies wavelet family | |
Chui et al. | Wavelet-based minimal-energy approach to image restoration |