R
Rafael Molina
Researcher at University of Granada
Publications - 398
Citations - 11970
Rafael Molina is an academic researcher from University of Granada. The author has contributed to research in topics: Image restoration & Iterative reconstruction. The author has an hindex of 52, co-authored 381 publications receiving 10765 citations. Previous affiliations of Rafael Molina include Intel & Northwestern University.
Papers
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Bayesian Blind Deconvolution From Differently Exposed Image Pairs
TL;DR: This paper addresses the problem of utilizing two such images in order to obtain an estimate of the original scene and presents a novel blind deconvolution algorithm for solving it in a hierarchical Bayesian framework.
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Analytical and clinical evaluation of an electrochemiluminescence immunoassay for the determination of CA 125
Huub E. van Ingen,Daniel W. Chan,Walter Hubl,Hayato Miyachi,Rafael Molina,L. Pitzel,Ruibal A,Jean C. Rymer,Ingrid Domke +8 more
TL;DR: The Elecsys CA 125 II assay is linear over a broad range, yields precise and accurate results, is free from interferences, and compares well with other assays.
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Robust and Low-Rank Representation for Fast Face Identification with Occlusions
TL;DR: An iterative method to address the face identification problem with block occlusion using a robust representation based on two characteristics in order to model contiguous errors effectively and shows that this joint characterization is effective for describing errors with spatial continuity.
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Remote Sensing Image Classification with Large Scale Gaussian Processes
TL;DR: In this paper, the authors proposed two efficient methodologies for Gaussian Process (GP) classification, including the standard random Fourier features approximation into GPC and a model which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones within a variational Bayes approach.
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A New Optical Density Granulometry-Based Descriptor for the Classification of Prostate Histological Images Using Shallow and Deep Gaussian Processes
Ángel E. Esteban,Miguel López-Pérez,Adrián Colomer,María A. Sales,Rafael Molina,Valery Naranjo +5 more
TL;DR: A novel family of morphological descriptors, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis and is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external database.