Bioucas Dias, J.[Jose]
* 2012: Segmentation and Detection of Colorectal Polyps Using Local Polynomial Approximation
* 2014: Digital phase-shifting holography based on sparse approximation of phase and amplitude
* 2014: Hyperspectral image superresolution: An edge-preserving convex formulation
* 2014: Phase imaging via sparse coding in the complex domain based on high-order SVD and nonlocal BM3D techniques
* 2014: Wavefront reconstruction in phase-shifting interferometry via sparse coding of amplitude and absolute phase
* 2015: Collaborative sparse regression using spatially correlated supports-Application to hyperspectral unmixing
* 2015: Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization, A
* 2016: Framework for Fast Image Deconvolution With Incomplete Observations, A
* 2016: Multiband Image Fusion Based on Spectral Unmixing
* 2017: Class-specific image denoising using importance sampling
* 2017: Class-specific poisson denoising by patch-based importance sampling
* 2017: Super-Resolution of Multispectral Multiresolution Images from a Single Sensor
* 2018: Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
* 2019: External Patch-Based Image Restoration Using Importance Sampling
* 2019: Novel Sharpening Approach for Superresolving Multiresolution Optical Images, A
Includes: Bioucas Dias, J.[Jose] Bioucas-Dias, J.[José] Bioucas-Dias, J. Bioucas-Dias, J.[Jose]
15 for Bioucas Dias, J.
Bioucas Dias, J.M.[Jose M.]
* 2005: Discontinuity Preserving Phase Unwrapping Using Graph Cuts
* 2005: Minimum Total Variation in 3D Ultrasound Reconstruction
* 2005: Phase Unwrapping via Graph Cuts
* 2006: Bayesian Wavelet-Based Image Deconvolution: A GEM Algorithm Exploiting a Class of Heavy-Tailed Priors
* 2006: Fast Sparse Multinomial Regression Applied to Hyperspectral Data
* 2007: Bayesian Oil Spill Segmentation of SAR Images Via Graph Cuts
* 2007: Blind Estimation of Motion Blur Parameters for Image Deconvolution
* 2007: Majorization-Minimization Algorithms for Wavelet-Based Image Restoration
* 2007: New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration, A
* 2007: Phase Unwrapping via Graph Cuts
* 2007: Two-Step Algorithms for Linear Inverse Problems with Non-Quadratic Regularization
* 2008: Adaptive Local Phase Approximations and Global Unwrapping
* 2008: Denoising of medical images corrupted by Poisson noise
* 2008: iterative algorithm for linear inverse problems with compound regularizers, An
* 2008: Phase unwrapping via diversity and graph cuts
* 2009: CAPE: combinatorial absolute phase estimation
* 2009: Multi-frequency Phase Unwrapping from Noisy Data: Adaptive Local Maximum Likelihood Approach
* 2009: Total variation restoration of speckled images using a split-bregman algorithm
* 2010: augmented Lagrangian approach to linear inverse problems with compound regularization, An
* 2010: Fast Image Recovery Using Variable Splitting and Constrained Optimization
* 2010: Frame-based deconvolution of Poissonian images using alternating direction optimization
* 2010: Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization
* 2010: Restoration of Poissonian Images Using Alternating Direction Optimization
* 2010: Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning
* 2011: Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems, An
* 2011: Feature Selection in Regression Tasks Using Conditional Mutual Information
* 2011: Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning
* 2011: Integration of Hyperspectral Image Classification and Unmixing for Active Learning
* 2011: Sparse Unmixing of Hyperspectral Data
* 2012: Alternating Direction Algorithm for Total Variation Reconstruction of Distributed Parameters, An
* 2012: Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
* 2012: Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing
* 2013: Generalized Composite Kernel Framework for Hyperspectral Image Classification
* 2013: Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning
* 2014: Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response
* 2014: Collaborative Sparse Regression for Hyperspectral Unmixing
* 2014: Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data
* 2014: MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression
* 2014: New Pansharpening Method Based on Spatial and Spectral Sparsity Priors, A
* 2014: Parametric Blur Estimation for Blind Restoration of Natural Images: Linear Motion and Out-of-Focus
* 2014: Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles
* 2014: Signal and Image Processing in Hyperspectral Remote Sensing
* 2014: Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing, A
* 2014: Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization
* 2015: HYCA: A New Technique for Hyperspectral Compressive Sensing
* 2015: Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation
* 2015: Interferometric Phase Image Estimation via Sparse Coding in the Complex Domain
* 2015: Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing
* 2015: Multiple Feature Learning for Hyperspectral Image Classification
* 2015: Pansharpening Based on Semiblind Deconvolution
* 2016: Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data
* 2016: Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects
* 2016: Image restoration and reconstruction using variable splitting and class-adapted image priors
* 2016: R-FUSE: Robust Fast Fusion of Multiband Images Based on Solving a Sylvester Equation
* 2016: Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing
* 2016: Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches
* 2017: Hyperspectral Image Denoising Based on Global and Non-Local Low-Rank Factorizations
* 2017: New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping, A
* 2017: Performance measures for classification systems with rejection
* 2017: Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm
* 2017: Sparse Distributed Multitemporal Hyperspectral Unmixing
* 2018: Convex Formulation for Multiband Image Classification With Superpixel-Based Spatial Regularization
* 2018: Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
* 2019: Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising, A
* 2019: Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing
* 2020: Explicit and Scene-Adapted Definition of Convex Self-Similarity Prior With Application to Unsupervised Sentinel-2 Super-Resolution, An
* 2020: Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion
* 2021: Block-Gaussian-Mixture Priors for Hyperspectral Denoising and Inpainting
* 2021: Hyperspectral Image Denoising Based on Global and Non-Local Low-Rank Factorizations
* 2022: Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations
Includes: Bioucas Dias, J.M.[Jose M.] Bioucas-Dias, J.M.[José M.] Bioucas-Dias, J.M. Bioucas-Dias, J.M.[Jose M.]
70 for Bioucas Dias, J.M.
Bioucas Dias, J.M.B.[Jose M.B.]
* 2005: Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?
* 2005: Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data
* 2007: Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning
* 2007: Dependent Component Analysis: A Hyperspectral Unmixing Algorithm
* 2008: Hyperspectral Subspace Identification
* 2011: Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning
* 2012: Hyperspectral Unmixing Based on Mixtures of Dirichlet Components
Includes: Bioucas Dias, J.M.B.[Jose M.B.] Bioucas-Dias, J.M.B.[José M.B.]
7 for Bioucas Dias, J.M.B.
Bioul, G.[Gery]
* 2016: Real-time speckle image processing