Multi-frequency image completion via a biologically-inspired sub-Riemannian model with frequency and phase
| Autor: | Baspinar, Emre |
|---|---|
| Přispěvatelé: | Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2021 |
| Předmět: |
Mathematics - Differential Geometry
FOS: Computer and information sciences Computer Vision and Pattern Recognition (cs.CV) Computer applications to medicine. Medical informatics R858-859.7 Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology image completion Article 03 medical and health sciences 0302 clinical medicine Photography 0202 electrical engineering electronic engineering information engineering FOS: Mathematics [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP] Radiology Nuclear Medicine and imaging Electrical and Electronic Engineering visual cortex differential geometry TR1-1050 Gabor function Quantitative Biology::Neurons and Cognition [SCCO.NEUR]Cognitive science/Neuroscience QA75.5-76.95 neurogeometry [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Computer Graphics and Computer-Aided Design sub-Riemannian geometry Differential Geometry (math.DG) [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG] Electronic computers. Computer science [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition 030217 neurology & neurosurgery |
| Zdroj: | Journal of Imaging Journal of Imaging, MDPI, 2021, 7 (12), pp.271. ⟨10.3390/jimaging7120271⟩ Journal of Imaging, Vol 7, Iss 271, p 271 (2021) Journal of Imaging; Volume 7; Issue 12; Pages: 271 |
| ISSN: | 2313-433X |
| DOI: | 10.48550/arxiv.2110.14330 |
| Popis: | International audience; We present a novel cortically-inspired image completion algorithm. It uses five-dimensional sub-Riemannian cortical geometry, modeling the orientation, spatial frequency and phase-selective behavior of the cells in the visual cortex. The algorithm extracts the orientation, frequency and phase information existing in a given two-dimensional corrupted input image via a Gabor transform and represents those values in terms of cortical cell output responses in the model geometry. Then, it performs completion via a diffusion concentrated in a neighborhood along the neural connections within the model geometry. The diffusion models the activity propagation integrating orientation, frequency and phase features along the neural connections. Finally, the algorithm transforms the diffused and completed output responses back to the two-dimensional image plane. |
| Databáze: | OpenAIRE |
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