19 January 2010 Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions
Author Affiliations +
Proceedings Volume 7537, Digital Photography VI; 753705 (2010); doi: 10.1117/12.838608
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States
Abstract
Accurate noise level estimation is essential to assure good performance of noise reduction filters. Noise contaminating raw images is typically modeled as additive white and Gaussian distributed (AWGN); however raw images are affected by a mixture of noise sources that overlap according to a signal dependent noise model. Hence, the assumption of constant noise level through all the dynamic range represents a simplification that does not allow precise sensor noise characterization and filtering; consequently, local noise standard deviation depends on signal levels measured at each location of the CFA (Color Filter Array) image. This work proposes a method for determining the noise curves that map each CFA signal intensity to its corresponding noise level, without the need of a controlled test environment and specific test patterns. The process consists in analyzing sets of heterogeneous raw CFA images, allowing noise characterization of any image sensor. In addition we show how the estimated noise level curves can be exploited to filter a CFA image, using an adaptive signal dependent Gaussian filter.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
A. Bosco, R. A. Bruna, D. Giacalone, S. Battiato, R. Rizzo, "Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions", Proc. SPIE 7537, Digital Photography VI, 753705 (19 January 2010); doi: 10.1117/12.838608; https://doi.org/10.1117/12.838608
PROCEEDINGS
10 PAGES


SHARE
KEYWORDS
Interference (communication)

Image filtering

Image sensors

Sensors

Image processing

Analog electronics

Electronic filtering

RELATED CONTENT


Back to Top