In this paper equations for the output mean and variance of the intensity-dependent spread (IDS) filter and its reconstruction are derived for images corrupted by white Gaussian and white Poisson noise. These equations are then used to compute the signal-to-noise ratio (SNR) in both the filtered image and the reconstructed image. We show that for additive Gaussian noise the SNR in the reconstructed image varies as the square root of the mean intensity and for additive Poisson noise the SNR is constant. We compare the SNR in the reconstructed image with that of the linear-shift-invariant Gaussian filter, and we show that the IDS filter can achieve a specified minimum SNR over the image with substantially less blur than a Gaussian. We also derive a formula to trade off blur for SNR by adjusting the spread factor of the IDS kernel.

© 1998 Optical Society of America

[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

• Prediction of human observer performance by numerical observers: an experimental study (JOSAA)
• Dynamics of turbulence-induced noise in image deconvolution with support constraint (JOSAA)
• Biomedical Optical Tomography Using Dynamic Parameterization and Bayesian Conditioning on Photon Migration Measurements (AO)
• Speckle Imaging and Hidden Phase (AO)
• Reduction of nonstationary noise in telescope imagery using a support constraint (OE)

Related Conference Papers

• The Quantized Cosine Transform for Sensor-Layer Image Compression
• The Quantized Cosine Transform for Sensor-Layer Image Compression
• Three-Dimensional Shape-Based Imaging in Diffuse Optical Tomography Using Spherical Harmonics
• A Novel Reconstruction Method for Weak-Phase Optical Interferometry
• Tunable Ultra-Narrow Band-Pass Filters Based on Volume Bragg Grating