원문정보
초록
영어
Adaptive filters are commonly used to remove disturbances corrupting a signal, particularly when a reference signal correlated to the disturbance is available. Accompanying random noise, sinusoidal disturbance or other undesired signals can be removed from the desired signal. When applied to images, the term adaptive filter commonly refers to a filter which changes its features based on the local statistical characteristics of the image and noise (within each image segment). It has also been proposed previously to use the LMS adaptive filter for 2-D image filtering, mostly for random noise removal. In this paper, it is offered to extend the use of the 2-D LMS adaptive filter to other image disturbances, such as a varying frequency sinusoidal disturbance, and the removal of an undesired added image from the desired one. Moreover, it is proposed here that in some cases it may be useful to convert the two-dimensional image to a one-dimensional signal (by chaining its row or columns), and apply a 1-D LMS adaptive filter. The 2-D LMS adaptive filter is demonstrated to work well in restoring an image corrupted by a random noise, a sinusoidal disturbance or a disturbance of an undesired image added to the desired one. In some particular conditions an adaptive filter with no reference signal may also be used to remove the undesired disturbance from the image.
목차
1. Introduction
2. Adaptive Filtering of 2-D Signals
2.1 Converting the 2-D images into 1-D Signals
2.2 Modifying the Adaptive Filter Update Equation to Cope with 2-D Filters and Image Data
3. Experimental Results
3.1 A Random White Noise Disturbance
3.2 A Sinusoidal Disturbance
3.3 A Superimposed Image Disturbance
3.4 Results for Applying a Single-input Adaptive Filter to Corrupted 2-D Images (No Reference Input)
4. Conclusion
References