Small Convolution Kernels for High-Fidelity Image Restoration

Stephen E. Reichenbac, Stephen K. Park

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


This paper develops an algorithm for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity (i.e., minimize the expected mean-square restoration error). The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

Original languageEnglish (US)
Pages (from-to)2263-2274
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number10
StatePublished - Oct 1991
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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