Publication:
Estudio de algoritmos de reducción de ruido en video

Thumbnail Image
Files

Files

Estudio de algoritmos de reduccion de ruido en video.pdf (388.76 KB)
View Statistics
Reference Managers
Mendeley
Indexers
Google Scholar CORE
QR Code
QR Code
Authors

Authors

Cancino Suárez, Sandra Liliana
ESTUPIÑÁN, ENRIQUE
Rodríguez Ramírez, Marcela Patricia
Makowski, Dominik
Abstract (Spanish)
En este artículo se describe la implementación de algoritmos para reducción de ruido. Estos algoritmos se aplican a videos con diferentes características de movimiento, esto es: bajo, medio o alto, realizando medidas objetivas y subjetivas para evaluar qué tan efectivos son, tanto en su implementación en el hardware DSP como en software. A pesar de las diferencias en los videos que se van a evaluar, el algoritmo que presenta mejores resultados en la medida objetiva es Alpha-Trimmed, incluso con mejores resultados en la implementación en hardware que en simulación. En la medida subjetiva el algoritmo más favorecido es el Mejor Vecino, aunque no por mucho margen.
Abstract (English)
This paper describes the implementation of noise reduction algorithms. These algorithms are tested on videos with different characteristics of low, medium and high movement, measuring objective and subjective measurements to evaluate their effectiveness either in DSP hardware or software implementation. Neither complexity nor easiness of implementation in real time is evaluated. In this case, when the algorithm increases its complexity it becomes more efficient in removing noise.
Extent
9 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/2523
Collections

Collections

AB - Ecitrónica
References

Quinnel, Richard (2009, September). Smarter video analysis Techniques mine more data. Electronic Design, Cover Feature.

Bovick, A. (2009). The essential guide of video processing, 2nd ed. Academic Press.

Hines, G., Rahman, Z., Jobson, D. & Woodell, G. DSP implementation of the Retinex Image Enhancement Algorithm.

Tsuen, M., Kasturi, R. & Sivasubramaniam, A. (2003). A pipeline-based approach for scheduling video processing algorithms on NOW. IEEE transactions on parallel and distributed systems, vol. 14, No. 2.

Gonzalez, R., Woods, R. & Steven, L. (2004). Eddins digital image processing using Matlab, Prentice Hall.

Gonzalez, R. & Woods, R. (2002). Digital image processing, 2nd ed. Prentice Hall.

Pratt, William K. (2001). Digital image processing: PIKS Inside, 3rd ed. John Wiley & Sons, Inc.

Bovik, A. (2000). Handbook of image and video processing, Academic Press.

Zlokolica, V., Philips, W. & Van De Ville, D. (2002). Robust non-linear filtering for video processing. Gent, Bélgica: Ghent University.

Espinosa-Duró, V., Faúndez-Zanuy, M. & Ortega, J.A. (2004). Face detection from a video camera image sequence. Polytechnic University of Catalonia.

Chen, T-H., Lin, Z-H., Chen, C-H. & Kao, C-L. (2007). A fast video noise reduction method by using object-based temporal filtering, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP.

Hakan, N., Bengt, O. & Youshi, X. Spatio-Temporal Noise Reduction Asic for Real-Time Video Processing, ITE Mid Sweden University.

Kanjanawanishkul, K. & Uyyanonvara, B. (2003). Fast Adaptive Algorithm for Time-Critical Color Quantization Application, Proc. VIIth Digital Image Computing: Techniques and Applications. Sun C., Talbot H., Ourselin S. and Adriaansen T. (eds.), 10-12 Dec. Sydney.