A Real-Time FHD Learning-Based Super-Resolution System Without a Frame Buffer

A Real-Time FHD Learning-Based Super-Resolution System Without a Frame Buffer

This brief presents a real-time learning-based superresolution (SR) system without a frame buffer. The system running on an Altera Stratix IV field programmable gate array can achieve output resolution of 1920 × 1080 (FHD) at 60 fps. The proposed architecture performs an anchored neighborhood regres...

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Veröffentlicht in:IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2017-12, Vol.64 (12), p.1407-1411
Hauptverfasser: Yang, Ming-Che, Liu, Kuan-Ling, Chien, Shao-Yi
Format: Artikel
Sprache:eng
Schlagworte:
anchored neighborhood regression
Field programmable gate arrays
FPGA
Image resolution
Interpolation
Learning systems
real-time
Real-time systems
Regression analysis
Super resolution
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Beschreibung
Zusammenfassung:This brief presents a real-time learning-based superresolution (SR) system without a frame buffer. The system running on an Altera Stratix IV field programmable gate array can achieve output resolution of 1920 × 1080 (FHD) at 60 fps. The proposed architecture performs an anchored neighborhood regression algorithm that generates a high-resolution image from a low-resolution image input using only numbers of line buffers. This real-time system without a frame buffer makes it possible to integrate SR operation into image sensors or display drivers carrying out computational photography and display.
ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2017.2749336