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Abstract

The ultimate goal of restoration techniques is to improve an image in some predefined sense. Restoration attempts to reconstruct or recover an image that has been degraded by using a prior knowledge of the degradation phenomenon.

Thus restoration techniques are oriented towards modeling the degradation and applying the inverse process order to recover the original image.

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation.

The observation signal space is partitioned based on the differences defined between the current pixel value and the outputs of CWM center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector.

The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise. Index Terms used are enter weighted median, least mean square, median filter, recursive filtering.

Image Enhancement is the ultimate goal of restoration techniques is to improve an image in some predefined sense. Restoration attempts to reconstruct or recover an image that has been degraded by using a prior knowledge of the degradation phenomenon.

Thus restoration techniques are oriented toward modeling the degradation and applying the inverse process in order to recover the original image. Image restoration methods are used to improve the appearance of an image by application of a restoration process that uses a mathematical model for image degradation. The use of the model is what differentiates restoration from enhancement.

Examples type of degradation:

• Blurring from motion or atmospheric disturbance

• Geometric distortion caused by imperfect lenses

• Superimposed interference pattern caused by

• Mechanical systems

• Noise from electronic sources

Image restoration removes or minimizes some known degradations in an image. It can be seen as a special kind of image enhancement. The most common degradations have their origin in imperfections of the sensors, or in transmission. It is assumed that a mathematical model of the degradation process is known, or that it can be derived by an analysis of other input images. The underlying idea is to model the degradation process and then apply the inverse process to restoe the original image.