Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions

A. Bosco; R. A. Bruna; D. Giacalone; S. Battiato; R. Rizzo

doi:10.1117/12.838608

18 January 2010 Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions

A. Bosco, R. A. Bruna, D. Giacalone, S. Battiato, R. Rizzo

Proceedings Volume 7537, Digital Photography VI; 753705 (2010) https://doi.org/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.

Citation Download Citation

A. Bosco, R. A. Bruna, D. Giacalone, S. Battiato, and R. Rizzo "Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions", Proc. SPIE 7537, Digital Photography VI, 753705 (18 January 2010); https://doi.org/10.1117/12.838608

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available