Efficient and fast SOP-based inpainting for neurological signals in resource limited systems
| Autor: | Steffen Paul, Dagmar Peters-Drolshagen, Benjamin Knoop, Heiner Lange, Pascal Seidel, Sebastian Schmale |
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| Rok vydání: | 2016 |
| Předmět: |
Matching (statistics)
Computer science business.industry 0206 medical engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Inpainting Pattern recognition 02 engineering and technology computer.file_format Energy consumption 020601 biomedical engineering JPEG 03 medical and health sciences 0302 clinical medicine Compression (functional analysis) JPEG 2000 Computer vision Artificial intelligence business computer Realization (systems) 030217 neurology & neurosurgery Transform coding |
| Zdroj: | ICECS |
| DOI: | 10.1109/icecs.2016.7841179 |
| Popis: | This work presents fast and efficient patch matching and ordering techniques for a novel inpainting-based compression and reconstruction methodology to continuously monitor neural activity. The mask-based compression is especially relevant for the technical realization of fully implantable neural measurement systems (NMS), because of restrictions regarding area and energy consumption. Novel approaches for decompression significantly reduce the number of computations for the procedure of smooth ordering patches (SOP) by a restricted neighboring search along consistent electrode patterns and by a patch group matching technique. Both combined yields a speed-up of 49.2x compared to an unrestricted patch search. With regard to recovered signal quality and compression of up to 95%, the proposed bridge mask achieves accurate results. The fast inpainting-based processing, including the proposed patch matching and ordering approaches, outperforms compression-focused standard techniques like JPEG and JPEG2000 regarding reconstruction quality of real measured neurological signals at high degrees of data reduction. |
| Databáze: | OpenAIRE |
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