Starck et al., 2021 - Google Patents
Weak-lensing mass reconstruction using sparsity and a Gaussian random fieldStarck et al., 2021
View HTML- Document ID
- 8561181310076861661
- Author
- Starck J
- Themelis K
- Jeffrey N
- Peel A
- Lanusse F
- Publication year
- Publication venue
- Astronomy & Astrophysics
External Links
Snippet
Aims. We introduce a novel approach to reconstructing dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components:(1) a …
- 238000004422 calculation algorithm 0 abstract description 30
Classifications
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
- G06T2211/424—Iterative
-
- 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]
-
- 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
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
-
- 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
-
- 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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4084—Transform-based scaling, e.g. FFT domain scaling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Starck et al. | Weak-lensing mass reconstruction using sparsity and a Gaussian random field | |
| Sun et al. | Scalable plug-and-play ADMM with convergence guarantees | |
| Dabbech et al. | Moresane: Model reconstruction by synthesis-analysis estimators-a sparse deconvolution algorithm for radio interferometric imaging | |
| Lanusse et al. | High resolution weak lensing mass mapping combining shear and flexion | |
| Jalobeanu et al. | Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method | |
| Bobin et al. | Sparse component separation for accurate cosmic microwave background estimation | |
| Regaldo-Saint Blancard et al. | A new approach for the statistical denoising of Planck interstellar dust polarization data | |
| Starck et al. | Low-ℓ CMB analysis and inpainting | |
| Sureau et al. | Deep learning for a space-variant deconvolution in galaxy surveys | |
| Ellien et al. | DAWIS: a detection algorithm with wavelets for intracluster light studies | |
| Massa et al. | Count-based imaging model for the Spectrometer/Telescope for Imaging X-rays (STIX) in Solar Orbiter | |
| Flasseur et al. | REXPACO: An algorithm for high contrast reconstruction of the circumstellar environment by angular differential imaging | |
| Liaudat et al. | Multi-CCD modelling of the point spread function | |
| Wang et al. | Multiplicative noise and blur removal by framelet decomposition and $ l_ {1} $-based L-curve method | |
| Nammour et al. | ShapeNet: Shape constraint for galaxy image deconvolution | |
| Leonard et al. | A compressed sensing approach to 3D weak lensing | |
| Fermanian et al. | PnP-ReG: Learned regularizing gradient for plug-and-play gradient descent | |
| Lyu et al. | Iterative reconstruction for low dose CT using Plug-and-Play alternating direction method of multipliers (ADMM) framework | |
| Li et al. | Diffusion posterior sampling for nonlinear CT reconstruction | |
| Price et al. | Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure | |
| Chen et al. | Hyper-Laplacian regularized non-local low-rank prior for blind image deblurring | |
| Lu et al. | Rician noise removal via a learned dictionary | |
| Yang et al. | Fractional‐order tensor regularisation for image inpainting | |
| Kansal | Reconstruction of weak lensing mass maps for non-Gaussian studies in the celestial sphere | |
| Tersenov et al. | Impact of weak-lensing mass-mapping algorithms on cosmology inference |