[go: up one dir, main page]

Prudyus et al., 2001 - Google Patents

Wavelet-based MAP image denoising using provably better class of stochastic iid image models

Prudyus et al., 2001

Document ID
58025568731927880
Author
Prudyus I
Voloshynovskiy S
Synyavskyy A
Publication year
Publication venue
5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service. TELSIKS 2001. Proceedings of Papers (Cat. No. 01EX517)

External Links

Snippet

The paper advocates a statistical approach to image denoising based on a maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of independent identically distributed stochastic image priors is considered to obtain a simple …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G06K9/6203Shifting or otherwise transforming the patterns to accommodate for positional errors
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • G06T2207/20064Wavelet transform [DWT]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • G06K9/52Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527Scale-space domain transformation, e.g. with wavelet analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • G06T3/4084Transform-based scaling, e.g. FFT domain scaling

Similar Documents

Publication Publication Date Title
Hou et al. NLH: A blind pixel-level non-local method for real-world image denoising
Sendur et al. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
Hyvärinen et al. Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation
Lebrun et al. A nonlocal Bayesian image denoising algorithm
Wang et al. Optimized feature extraction for learning-based image steganalysis
Luisier et al. A CURE for noisy magnetic resonance images: Chi-square unbiased risk estimation
Malladi et al. Image denoising using superpixel-based PCA
CN111932468A (en) Bayesian image denoising method based on noise-containing image distribution constraint
Onur Improved image denoising using wavelet edge detection based on Otsu’s thresholding
Yin et al. Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain
Jiang et al. Efficient noise-level estimation based on principal image texture
Dhaka et al. Likelihood estimation and wavelet transformation based optimization for minimization of noisy pixels
Campisi et al. A multiresolution approach for texture synthesis using the circular harmonic functions
Mayo et al. Comparing methods to denoise mammographic images
CN105894462B (en) Remote sensing images denoising method based on shearing wave zone Hidden markov tree model
Prudyus et al. Wavelet-based MAP image denoising using provably better class of stochastic iid image models
Synyavskyy et al. Wavelet-based map image denoising using provably better class of stochastic iid image models
Leung et al. Maximum a posteriori spatial probability segmentation
Krajsek et al. The edge preserving wiener filter for scalar and tensor valued images
Alshawi et al. Magnetic resonance and computed tomography image fusion using bidimensional empirical mode decomposition
Ting et al. A novel approach for arbitrary-shape roi compression of medical images using principal component analysis (pca)
Solangi et al. Image denoising methods: Literature review
Zhang et al. Joint image denoising using self-similarity based low-rank approximations
Pei et al. Recursive order-statistic soft morphological filters
Mary et al. Wavelets and ridgelets for biomedical image denoising