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Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
February 1985 (vol. 7 no. 2)
pp. 165-177
Darwin T. Kuan, Central Engineering Laboratories, FMC Corporation, Santa Clara, CA 95052.
Alexander A. Sawchuk, Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089.
Timothy C. Strand, IBM Corporation, San Jose, CA 95193.
Pierre Chavel, Institut d'Optique, Universite de Paris-Sud, Orsay, France.
In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm [16]. The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.
Darwin T. Kuan, Alexander A. Sawchuk, Timothy C. Strand, Pierre Chavel, "Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 2, pp. 165-177, Feb. 1985, doi:10.1109/TPAMI.1985.4767641
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