Impulse Noise Removal in Different Types of Color Image using DWT and Threshold Filter

Abstract

Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme for the removal of random-valued impulse noise. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Noise elimination is the main constraint in digital image processing and sometimes it is very difficult to find out the origin of the noise. We have employed a novel thresholding rule based on wavelet transform for impulse noise reduction from color images. The wavelet transform performs multiscale analysis of the given image by treating different frequency components present in an image separately. The wavelet transform decomposes the given image into detail and approximation sub-band. It is assumed that approximation sub-band contains significant structures whereas detail sub-band may contain noise. So, the filtering process is used more for detail sub-band and less in approximation sub-band. It is observed that the proposed hybrid method gives better results for impulse noise reduction, edge preservation and feature preservation for color images.