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ORCIDRafael Molina 0001
Rafael Molina Soriano
Person information
- affiliation: University of Granada, Department of Computer Science and Artificial Intelligence, Spain
Other persons with the same name
- Rafael Molina 0002
![[0000-0002-6443-4989]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-6443-4989")
— Institute of Physical Chemistry Rocasolano, Deparment of Crystallography and Structural Biology, Madrid, Spain - Rafael Molina 0003 — Illinois Institute of Technology, Chicago, IL, USA
- Rafael Molina 0004 — Castilla La Mancha University, Spain
- Rafael Molina 0005 (aka: Rafael Molina Sánchez) — Technical University of Madrid, Harbor Research Laboratory, Spain
- Rafael Molina 0006 — Universidad Distrital Francisco José de Caldas, Bogotá, Colombia
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2020 – today
- 2024
[j97]Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos:
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection. Artif. Intell. 331: 104115 (2024)- [j96]Fernando Pérez-Bueno, Kjersti Engan, Rafael Molina:
Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD. Artif. Intell. Medicine 156: 102969 (2024) - [j95]Neel Kanwal, Miguel López-Pérez, Umay Kiraz, Tahlita C. M. Zuiverloon, Rafael Molina, Kjersti Engan:
Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images. Comput. Medical Imaging Graph. 112: 102321 (2024) - [j94]Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Christopher Felicelli, Jeffery A. Goldstein, Brian Vadasz, Rafael Molina, Aggelos K. Katsaggelos:
Learning from crowds for automated histopathological image segmentation. Comput. Medical Imaging Graph. 112: 102327 (2024) - [j93]Miguel López-Pérez, Alba Morquecho, Arne Schmidt, Fernando Pérez-Bueno, Aurelio Martín-Castro, Javier Mateos, Rafael Molina:
The CrowdGleason dataset: Learning the Gleason grade from crowds and experts. Comput. Methods Programs Biomed. 257: 108472 (2024) - [j92]Shuowen Yang, Fernando Pérez-Bueno, Francisco M. Castro-Macías, Rafael Molina, Aggelos K. Katsaggelos:
BCD-net: Stain separation of histological images using deep variational Bayesian blind color deconvolution. Digit. Signal Process. 145: 104318 (2024) - [j91]Jose Pérez-Cano, Yunan Wu, Arne Schmidt, Miguel López-Pérez, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos:
An end-to-end approach to combine attention feature extraction and Gaussian Process models for deep multiple instance learning in CT hemorrhage detection. Expert Syst. Appl. 240: 122296 (2024) - [j90]Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper, Lee A. Newberg, Andinet Enquobahrie, Rafael Molina, Aggelos K. Katsaggelos:
Focused active learning for histopathological image classification. Medical Image Anal. 95: 103162 (2024) - [j89]Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina:
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. Pattern Recognit. 146: 110057 (2024) - [j88]Xinyi Wu, Santiago López-Tapia, Xijun Wang, Rafael Molina, Aggelos K. Katsaggelos:
Real-Time Lightweight Video Super-Resolution With RRED-Based Perceptual Constraint. IEEE Trans. Circuits Syst. Video Technol. 34(10): 10310-10325 (2024) - [j87]Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina:
Probabilistic Attention Based on Gaussian Processes for Deep Multiple Instance Learning. IEEE Trans. Neural Networks Learn. Syst. 35(8): 10909-10922 (2024) - [c133]Francisco M. Castro-Macías, Fernando Pérez-Bueno, Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Blind Color Deconvolution and Classification of Histological Images Using the Hyperbolic Secant Prior. ISBI 2024: 1-5 - [i18]Xijun Wang, Santiago López-Tapia, Alice Lucas, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos:
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models. CoRR abs/2403.10589 (2024) - [i17]Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos:
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detection. CoRR abs/2403.14829 (2024) - [i16]Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper, Lee A. Newberg, Andinet Enquobahrie, Aggelos K. Katsaggelos, Rafael Molina:
Focused Active Learning for Histopathological Image Classification. CoRR abs/2404.04663 (2024) - [i15]Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos:
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification. CoRR abs/2410.03276 (2024) - 2023
- [j86]Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos:
Deep Gaussian Processes for Classification With Multiple Noisy Annotators. Application to Breast Cancer Tissue Classification. IEEE Access 11: 6922-6934 (2023) - [j85]Rocío del Amor, Jose Pérez-Cano, Miguel López-Pérez, Liria Terradez, José Aneiros-Fernández, Sandra Morales, Javier Mateos, Rafael Molina, Valery Naranjo:
Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. Artif. Intell. Medicine 145: 102686 (2023) - [j84]Santiago López-Tapia, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Learning Moore-Penrose based residuals for robust non-blind image deconvolution. Digit. Signal Process. 142: 104193 (2023) - [j83]Pablo Ruiz, Pablo Morales-Álvarez, Scott Coughlin, Rafael Molina, Aggelos K. Katsaggelos:
Probabilistic fusion of crowds and experts for the search of gravitational waves. Knowl. Based Syst. 261: 110183 (2023) - [c132]Fernando Pérez-Bueno, Kjersti Engan, Rafael Molina:
A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H &E Histological Images. AIME 2023: 207-217 - [c131]Miguel López-Pérez, Pablo Morales-Álvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos:
Crowdsourcing Segmentation of Histopathological Images Using Annotations Provided by Medical Students. AIME 2023: 245-249 - [c130]Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina:
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation. ICCV 2023: 21040-21049 - [c129]Shuowen Yang, Fernando Pérez-Bueno, Francisco M. Castro-Macías, Rafael Molina, Aggelos K. Katsaggelos:
Deep Bayesian Blind Color Deconvolution of Histological Images. ICIP 2023: 710-714 - [c128]Santiago López-Tapia, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Deep Robust Image Restoration Using the Moore-Penrose Blur Inverse. ICIP 2023: 775-779 - [c127]Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos:
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection. MICCAI (5) 2023: 327-337 - [d1]Fernando Pérez-Bueno, Rafael Molina Soriano:
Practicas Jupyter para Extracción de Carácteristicas en Imágenes (Master DATCOM 21/22). Zenodo, 2023
- [i14]Fernando Pérez-Bueno, Luz García, Gabriel Maciá-Fernández, Rafael Molina:
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection. CoRR abs/2302.01759 (2023) - [i13]Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina:
Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning. CoRR abs/2302.04061 (2023) - [i12]Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez, Rafael Molina, Aggelos K. Katsaggelos:
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection. CoRR abs/2307.09457 (2023) - [i11]Arne Schmidt, Pablo Morales-Álvarez, Rafael Molina:
Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation. CoRR abs/2307.11397 (2023) - [i10]Pablo Morales-Álvarez, Arne Schmidt, José Miguel Hernández-Lobato, Rafael Molina:
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images. CoRR abs/2310.19359 (2023) - 2022
- [j82]Arne Schmidt, Julio Silva-Rodríguez, Rafael Molina, Valery Naranjo:
Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning. IEEE Access 10: 9763-9773 (2022) - [j81]Neel Kanwal, Fernando Pérez-Bueno, Arne Schmidt, Kjersti Engan, Rafael Molina:
The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review. IEEE Access 10: 58821-58844 (2022) - [j80]Julio Silva-Rodríguez, Arne Schmidt, María Á. Sales, Rafael Molina, Valery Naranjo:
Proportion constrained weakly supervised histopathology image classification. Comput. Biol. Medicine 147: 105714 (2022) - [j79]Fernando Pérez-Bueno, Juan G. Serra, Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Bayesian K-SVD for H and E blind color deconvolution. Applications to stain normalization, data augmentation and cancer classification. Comput. Medical Imaging Graph. 97: 102048 (2022) - [j78]Miguel López-Pérez, Arne Schmidt, Yunan Wu, Rafael Molina, Aggelos K. Katsaggelos:
Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection. Comput. Methods Programs Biomed. 219: 106783 (2022) - [j77]Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin, Rafael Molina, Aggelos K. Katsaggelos:
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1534-1551 (2022) - [j76]Fernando Pérez-Bueno, Luz García, Gabriel Maciá-Fernández, Rafael Molina:
Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection. IEEE/ACM Trans. Netw. 30(3): 1217-1229 (2022) - 2021
- [j75]Fernando Pérez-Bueno, Miguel Vega, María Á. Sales, José Aneiros-Fernández, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos:
Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference. Comput. Methods Programs Biomed. 211: 106453 (2021) - [j74]Santiago López-Tapia, Rafael Molina, Aggelos K. Katsaggelos:
Deep learning approaches to inverse problems in imaging: Past, present and future. Digit. Signal Process. 119: 103285 (2021) - [j73]Miguel López-Pérez, Luz García, M. Carmen Benítez, Rafael Molina:
A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes. IEEE Trans. Geosci. Remote. Sens. 59(5): 3875-3890 (2021) - [c126]Pablo Morales-Alvarez, Daniel Hernández-Lobato, Rafael Molina, José Miguel Hernández-Lobato:
Activation-level uncertainty in deep neural networks. ICLR 2021 - [c125]Yunan Wu, Arne Schmidt, Enrique Hernández-Sánchez, Rafael Molina, Aggelos K. Katsaggelos:
Combining Attention-Based Multiple Instance Learning and Gaussian Processes for CT Hemorrhage Detection. MICCAI (2) 2021: 582-591 - [i9]Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas, Rafael Molina, Gustau Camps-Valls:
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion. CoRR abs/2104.10638 (2021) - [i8]Julio Silva-Rodríguez, Adrián Colomer, María Á. Sales, Rafael Molina, Valery Naranjo:
Going Deeper through the Gleason Scoring Scale: An Automatic end-to-end System for Histology Prostate Grading and Cribriform Pattern Detection. CoRR abs/2105.10490 (2021) - 2020
- [j72]Julio Silva-Rodríguez, Adrián Colomer, María Á. Sales, Rafael Molina, Valery Naranjo:
Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Comput. Methods Programs Biomed. 195: 105637 (2020) - [j71]Fernando Pérez-Bueno, Miguel López-Pérez, Miguel Vega, Javier Mateos, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos:
A TV-based image processing framework for blind color deconvolution and classification of histological images. Digit. Signal Process. 101: 102727 (2020) - [j70]Santiago López-Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos:
A single video super-resolution GAN for multiple downsampling operators based on pseudo-inverse image formation models. Digit. Signal Process. 104: 102801 (2020) - [j69]Fernando Pérez-Bueno, Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors. Sensors 20(18): 5308 (2020) - [j68]Natalia Hidalgo-Gavira, Javier Mateos, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Variational Bayesian Blind Color Deconvolution of Histopathological Images. IEEE Trans. Image Process. 29: 2026-2036 (2020) - [j67]Xu Zhou, Rafael Molina, Yi Ma, Tianfu Wang, Dong Ni:
Parameter-Free Gaussian PSF Model for Extended Depth of Field in Brightfield Microscopy. IEEE Trans. Image Process. 29: 3227-3238 (2020) - [c124]Santiago López-Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos:
Gated Recurrent Networks for Video Super Resolution. EUSIPCO 2020: 700-704 - [c123]Fernando Pérez-Bueno, Miguel Vega, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos:
Fully Automatic Blind Color Deconvolution of Histological Images Using Super Gaussians. EUSIPCO 2020: 1254-1258 - [c122]Fernando Pérez-Bueno, Miguel Vega, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos:
Super Gaussian Priors for Blind Color Deconvolution of Histological Images. ICIP 2020: 3010-3014 - [i7]Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina, Gustau Camps-Valls:
Deep Gaussian Processes for geophysical parameter retrieval. CoRR abs/2012.12099 (2020)
2010 – 2019
- 2019
- [j66]Ángel E. Esteban, Miguel López-Pérez, Adrián Colomer, María Á. Sales, Rafael Molina, Valery Naranjo:
A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes. Comput. Methods Programs Biomed. 178: 303-317 (2019) - [j65]Juan G. Serra, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Variational EM method for blur estimation using the spike-and-slab image prior. Digit. Signal Process. 88: 116-129 (2019) - [j64]Pablo Morales-Alvarez, Pablo Ruiz, Raúl Santos-Rodríguez, Rafael Molina, Aggelos K. Katsaggelos:
Scalable and efficient learning from crowds with Gaussian processes. Inf. Fusion 52: 110-127 (2019) - [j63]Pablo Ruiz, Pablo Morales-Alvarez, Rafael Molina, Aggelos K. Katsaggelos:
Learning from crowds with variational Gaussian processes. Pattern Recognit. 88: 298-311 (2019) - [j62]Santiago López-Tapia, Rafael Molina, Nicolás Pérez de la Blanca:
Deep CNNs for Object Detection Using Passive Millimeter Sensors. IEEE Trans. Circuits Syst. Video Technol. 29(9): 2580-2589 (2019) - [j61]Alice Lucas, Santiago Lopez Tapia, Rafael Molina, Aggelos K. Katsaggelos:
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution. IEEE Trans. Image Process. 28(7): 3312-3327 (2019) - [c121]Santiago Lopez Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos:
Multiple-Degradation Video Super-Resolution with Direct Inversion of the Low-Resolution Formation Model. EUSIPCO 2019: 1-5 - [c120]Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Variational Bayes Color Deconvolution with a Total Variation Prior. EUSIPCO 2019: 1-5 - [c119]Xijun Wang, Alice Lucas, Santiago Lopez Tapia, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos:
A Composite Discriminator for Generative Adversarial Network based Video Super-Resolution. EUSIPCO 2019: 1-5 - [c118]Xinyi Wu, Alice Lucas, Santiago López-Tapia, Xijun Wang, Yul Hee Kim, Rafael Molina, Aggelos K. Katsaggelos:
Semantic Prior Based Generative Adversarial Network for Video Super-Resolution. EUSIPCO 2019: 1-5 - [c117]Xijun Wang, Alice Lucas, Santiago Lopez Tapia, Xinyi Wu, Rafael Molina, Aggelos K. Katsaggelos:
Spatially Adaptive Losses for Video Super-resolution with GANs. ICASSP 2019: 1697-1701 - [c116]Santiago López-Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos:
Gan-Based Video Super-Resolution With Direct Regularized Inversion of the Low-Resolution Formation Model. ICIP 2019: 2886-2890 - [c115]Alice Lucas, Santiago López-Tapia, Rafael Molina, Aggelos K. Katsaggelos:
Efficient Fine-Tuning of Neural Networks for Artifact Removal in Deep Learning for Inverse Imaging Problems. ICIP 2019: 3591-3595 - [c114]Miguel López-Pérez, Adrián Colomer, María Á. Sales, Rafael Molina, Valery Naranjo:
Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Granulometry-Based Descriptor. IDEAL (1) 2019: 39-46 - [i6]Santiago López-Tapia, Alice Lucas, Rafael Molina, Aggelos K. Katsaggelos:
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models. CoRR abs/1907.01399 (2019) - [i5]Pablo Morales-Alvarez, Pablo Ruiz, Scott Coughlin, Rafael Molina, Aggelos K. Katsaggelos:
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO. CoRR abs/1911.01915 (2019) - [i4]Alice Lucas, Santiago Lopez Tapia, Rafael Molina, Aggelos K. Katsaggelos:
Self-Supervised Fine-tuning for Image Enhancement of Super-Resolution Deep Neural Networks. CoRR abs/1912.12879 (2019) - 2018
- [j60]Salvador Villena, Miguel Vega, Javier Mateos, Duska Rosenberg, Fionn Murtagh, Rafael Molina, Aggelos K. Katsaggelos:
Image super-resolution for outdoor digital forensics. Usability and legal aspects. Comput. Ind. 98: 34-47 (2018) - [j59]Santiago Lopez Tapia, Rafael Molina, Nicolas Pérez de la Blanca:
Using machine learning to detect and localize concealed objects in passive millimeter-wave images. Eng. Appl. Artif. Intell. 67: 81-90 (2018) - [j58]Neda Rohani, Pablo Ruiz, Rafael Molina, Aggelos K. Katsaggelos:
Variational Gaussian process for multisensor classification problems. Pattern Recognit. Lett. 116: 80-87 (2018) - [j57]Alice Lucas, Michael Iliadis, Rafael Molina, Aggelos K. Katsaggelos:
Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods. IEEE Signal Process. Mag. 35(1): 20-36 (2018) - [j56]Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls:
Remote Sensing Image Classification With Large-Scale Gaussian Processes. IEEE Trans. Geosci. Remote. Sens. 56(2): 1103-1114 (2018) - [c113]Adrián Colomer, Pablo Ruiz, Valery Naranjo, Rafael Molina, Aggelos K. Katsaggelos:
Hard Exudate Detection Using Local Texture Analysis and Gaussian Processes. ICIAR 2018: 639-649 - [c112]Alice Lucas, Aggelos K. Katsaggelos, Santiago Lopez Tapia, Rafael Molina:
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution. ICIP 2018: 51-55 - [c111]Natalia Hidalgo-Gavira, Javier Mateos, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach. ICIP 2018: 983-987 - [c110]Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Rafael Molina, Gustau Camps-Valls:
Deep Gaussian Processes for Geophysical Parameter Retrieval. IGARSS 2018: 6175-6178 - [c109]Natalia Hidalgo-Gavira, Javier Mateos, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Fully Automated Blind Color Deconvolution of Histopathological Images. MICCAI (2) 2018: 183-191 - [i3]Alice Lucas, Santiago Lopez Tapia, Rafael Molina, Aggelos K. Katsaggelos:
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution. CoRR abs/1806.05764 (2018) - 2017
- [j55]Xu Zhou, Miguel Vega, Fugen Zhou, Rafael Molina, Aggelos K. Katsaggelos:
Fast Bayesian blind deconvolution with Huber Super Gaussian priors. Digit. Signal Process. 60: 122-133 (2017) - [j54]Michael Iliadis, Haohong Wang, Rafael Molina, Aggelos K. Katsaggelos:
Robust and Low-Rank Representation for Fast Face Identification With Occlusions. IEEE Trans. Image Process. 26(5): 2203-2218 (2017) - [j53]Juan G. Serra, Matteo Testa, Rafael Molina, Aggelos K. Katsaggelos:
Bayesian K-SVD Using Fast Variational Inference. IEEE Trans. Image Process. 26(7): 3344-3359 (2017) - [c108]Juan G. Serra, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Parameter estimation in spike and slab variational inference for blind image deconvolution. EUSIPCO 2017: 1495-1499 - [c107]Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls, Aggelos K. Katsaggelos:
Passive millimeter wave image classification with large scale Gaussian processes. ICIP 2017: 370-374 - [c106]Juan G. Serra, Salvador Villena, Rafael Molina, Aggelos K. Katsaggelos:
Greedy Bayesian double sparsity dictionary learning. ICIP 2017: 1935-1939 - [c105]Juan G. Serra, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Spike and slab variational inference for blind image deconvolution. ICIP 2017: 3765-3769 - [c104]Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls:
Efficient remote sensing image classification with Gaussian processes and Fourier features. IGARSS 2017: 2227-2230 - [i2]Pablo Morales-Alvarez, Adrian Perez-Suay, Rafael Molina, Gustau Camps-Valls:
Remote Sensing Image Classification with Large Scale Gaussian Processes. CoRR abs/1710.00575 (2017) - 2016
- [j52]Wael AlSaafin, Salvador Villena, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Compressive sensing super resolution from multiple observations with application to passive millimeter wave images. Digit. Signal Process. 50: 180-190 (2016) - [j51]Pablo Ruiz Matarán, Rafael Molina, Aggelos K. Katsaggelos:
Joint Data Filtering and Labeling Using Gaussian Processes and Alternating Direction Method of Multipliers. IEEE Trans. Image Process. 25(7): 3059-3072 (2016) - [c103]Wael Saafin, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Compressed sensing super resolution of color images. EUSIPCO 2016: 1563-1567 - [c102]Emre Besler, Pablo Ruiz, Rafael Molina, Aggelos K. Katsaggelos:
Classification of multiple annotator data using variational Gaussian process inference. EUSIPCO 2016: 2025-2029 - [c101]Santiago Lopez Tapia, Rafael Molina, Nicolas Pérez de la Blanca:
Detection and localization of objects in Passive Millimeter Wave Images. EUSIPCO 2016: 2101-2105 - [c100]Juan G. Serra, Pablo Ruiz, Rafael Molina, Aggelos K. Katsaggelos:
Bayesian logistic regression with sparse general representation prior for multispectral image classification. ICIP 2016: 1893-1897 - [c99]Javier Mateos, Antonio López, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos:
Multiframe blind deconvolution of passive millimeter wave images using variational dirichlet blur kernel estimation. ICIP 2016: 2678-2682 - [c98]Pablo Ruiz, Emre Besler, Rafael Molina, Aggelos K. Katsaggelos:
Variational Gaussian process for missing label crowdsourcing classification problems. MLSP 2016: 1-6 - [i1]Michael Iliadis, Haohong Wang, Rafael Molina, Aggelos K. Katsaggelos:
Robust and Low-Rank Representation for Fast Face Identification with Occlusions. CoRR abs/1605.02266 (2016) - 2015
- [j50]Pablo Ruiz, Xu Zhou, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos:
Variational Bayesian Blind Image Deconvolution: A review. Digit. Signal Process. 47: 116-127 (2015) - [j49]Aggelos K. Katsaggelos, Sara Bahaadini, Rafael Molina:
Audiovisual Fusion: Challenges and New Approaches. Proc. IEEE 103(9): 1635-1653 (2015) - [j48]Xu Zhou, Javier Mateos, Fugen Zhou, Rafael Molina, Aggelos K. Katsaggelos:
Variational Dirichlet Blur Kernel Estimation. IEEE Trans. Image Process. 24(12): 5127-5139 (2015) - [j47]Zhaofu Chen, Rafael Molina, Aggelos K. Katsaggelos:
Robust Recovery of Temporally Smooth Signals From Under-Determined Multiple Measurements. IEEE Trans. Signal Process. 63(7): 1779-1791 (2015) - [c97]