Title :
Self-adapting compressive image sensing scheme
Author :
Laiho, Mika ; Poikonen, Jonne ; Virtanen, Kati ; Paasio, Ari
Author_Institution :
Microelectron. Lab., Turku Univ., Turku
Abstract :
In this paper we propose a self-adapting image sensing scheme that compresses a wide dynamic range scene so that important visual characteristics are preserved throughout the perceived dynamic range. The exposure times of sensors are made adaptive to local illumination conditions while signal is being integrated. Resistive filtering with locally adaptive resistance values is performed to extract the local illuminance. The resistances need to be a adaptive only by the neighbor difference since at the sampling instant the filter always resides on the same operating point. Also, the accuracy of the filter does not need to match that of a high dynamic range input, but the low dynamic range reflectance. The relaxed accuracy requirements make possible a dense analog implementation of the filter. A conceptual schematic of a circuit realization of the scheme is shown and Matlab simulations are used to illustrate the operation.
Keywords :
filtering theory; image processing; image sensors; compressive image sensing scheme; resistive filtering; self-adapting image sensing; Adaptive filters; Dynamic range; Filtering; Image coding; Layout; Lighting; Matched filters; Reflectivity; Sampling methods; Sensor phenomena and characterization;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2008. CNNA 2008. 11th International Workshop on
Conference_Location :
Santiago de Compostela
Print_ISBN :
978-1-4244-2089-6
Electronic_ISBN :
978-1-4244-2090-2
DOI :
10.1109/CNNA.2008.4588663