G
Gabriel Cristóbal
Researcher at Spanish National Research Council
Publications - 168
Citations - 3517
Gabriel Cristóbal is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Image processing & Rényi entropy. The author has an hindex of 28, co-authored 166 publications receiving 3094 citations. Previous affiliations of Gabriel Cristóbal include Center for Strategic and International Studies & International Computer Science Institute.
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Proceedings ArticleDOI
Diatom autofocusing in brightfield microscopy: a comparative study
TL;DR: Two sound methods based on a modified Tenengrad and a modified Laplacian method are investigated and measurements show that they provide a reliable and suitable focus measure that outperform similar methods.
Journal ArticleDOI
Blind image quality assessment through anisotropy
TL;DR: Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images, and it is shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.
Journal ArticleDOI
Self-Invertible 2D Log-Gabor Wavelets
TL;DR: The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images to make it a promising tool for processing natural images.
Journal ArticleDOI
Identification of tuberculosis bacteria based on shape and color
TL;DR: A new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples.
Journal ArticleDOI
A Unified Approach to Superresolution and Multichannel Blind Deconvolution
TL;DR: A new approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene by building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains.