Nonlinear filtering structure for image smoothing in mixed-noise environments

Abstract

This paper introduces a new nonlinear filtering structure for filtering image data that have been corrupted by both impulsive and nonimpulsive additive noise. Like other nonlinear filters, the proposed filtering structure uses order-statistic operations to remove the effects of the impulsive noise. Unlike other filters, however, nonimpulsive noise is smoothed by using a maximum a posterior/ estimation criterion. The prior model for the image is a novel Markov random-field model that models image edges so that they are accurately estimated while additive Gaussian noise is smoothed. The Markov random-field-based prior is chosen such that the filter has desirable analytical and computational properties. The estimate of the signal value is obtained at the unique minimum of the a posterior/log likelihood function. This function is convex so that the output of the filter can be easily computed by using either digital or analog computational methods. The effects of the various parameters of the model will be discussed, and the choice of the predetection order statistic filter will also be examined. Example outputs under various noise conditions will be given.

Variations on Impulse Noise Model in Digital Image Processing Field: A Survey on Current Research Inclination

“…There are a lot of noise types normally corrupt digital image [5]. One of these noise types is the impulse noise [6]. The authors are with the School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Seberang Perai, Penang, Malaysia (e-mail: tshoong81@yahoo.com; haidi_ibrahim@ieee.org).…”