Pixel saturation, in which the incident light at a pixel causes one of the color channels of the camera sensor to respond at its maximum value, can produce undesirable artifacts in digital color images. We present a Bayesian algorithm that estimates what the saturated channel’s value would have been in the absence of saturation. The algorithm uses the nonsaturated responses from the other color channels, together with a multivariate normal prior that captures the correlation in response across color channels. The prior may be estimated directly from the image data, since most image pixels are not saturated. Given the prior and the responses of the nonsaturated channels, the algorithm returns the optimal expected mean square estimate for the true response. Extensions of the algorithm to the case in which more than one channel is saturated are also discussed. Both simulations and examples with real images are presented to show that the algorithm is effective.

© 2004 Optical Society of America

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

Related Journal Articles

• Further study on the convergence property of the hybrid input–output algorithm used for phase retrieval (JOSAA)
• Developments in generalized ranked-order filters (JOSAA)
• System Design for Accurately Estimating the Spectral Reflectance of Art Paintings (AO)
• Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements (AO)
• Spectral image reconstruction using an edge preserving spatio-spectral Wiener estimation (JOSAA)

Related Conference Papers

• Diffraction-Limited Impulse Response Image Reconstruction with a Single Imaging Pixel
• Diffraction-Limited Impulse Response Image Reconstruction with a Single Imaging Pixel
• The Quantized Cosine Transform for Sensor-Layer Image Compression
• Iterative Exposure Bracketing
• Neural Network for the Digital Cleaning of an Oil Painting