22-03-2014, 04:45 PM
Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter
ABSTRACT
Many denoising algorithms have been proposed to recover a noise corrupted image. However, most of
them cannot well recover a heavy noise corrupted image with noise density above 70%. In this Letter,
we propose a new approach to efficiently remove background noise by detecting and modifying noisy
pixels in an image. If the center pixel of a local window is classified to noisy, this center pixel is replaced
by a weighted median value on an optimum direction, enabling impulse noise to be removed. Conversely,
the center pixel is kept unchanged when it is classified to noise-free, yielding the quality of restored
image being well maintained. Experimental results show that the proposed approach cannot only effi-
ciently suppress high-density impulse noise, but also can well preserve the detailed information of an
image.
Introduction
Impulse noise in an image is present due to bit errors in trans-
mission or introduced during the signal acquisition stage. There are
two types of impulse noise, they are salt-and-pepper noise and
random valued noise. Salt-and-pepper noise can corrupt images
where the corrupted pixel takes either maximum or minimum
gray level. This noise can significantly deteriorate the quality of
an image. How to efficiently remove this kind of impulse noise is
an important research task.
Many nonlinear filters have been proposed for the restoration of
images contaminated by salt-and-pepper noise (Chen et al., 1999;
Chan et al., 1999; Chen and Lien, 2008; Akkoul et al., 2010; Toh and
Isa, 2010; Wan et al., 2010; Duan and Zhang, 2010; Hwang and
Hadded, 1995; Sun and Neuvo, 1994; Zhang and Karim, 2002).
Most of these standard median filters have been established as a
reliable method to remove salt-and-pepper noise without damag-
ing the edge details. However, the major drawback of standard
median filter is that the filter is only effective to work at low noise
densities.
Proposed approach
The block diagram of proposed method is shown in Fig. 1. Ini-
tially, a noise-corrupted image is analyzed by a 7 Â 7 sliding win-
dow. If the value of the center pixel in a local window is not an
extreme value (0 or 255), the center pixel is classified to noise-free
and kept unchanged to maintain image quality. Conversely, the
center pixel needs to be further classified to which it is a noise pix-
el or one of an edge pixel. If the center pixel belongs to an edge, this
pixel is still kept unchanged. On the contrary, it is a noise pixel and
should be modified by the directional-weighted-median (DWM)
filter. The pixels with extreme value (0 or 255) should be excluded
before taking the DWM filtering, enabling the salt-and-pepper
noise to be thoroughly removed.
Conclusions
An improved version of the directional-weighted-median
(DWM) filter was proposed in this study. The major reason why
the proposed method can significantly improve the performance
of the directional-weighted-median (DWM) filter is to include
additional directions (12 directions) for edge detection, where
the DWM filter only employs four directions. These additional
directions improve the accuracy of edge detection. In addition, a
pixel with an extreme value (0 or 255 for an 8-bits gray-level im-
age) were excluded before median filtering on the optimum direc-
tion, yielding the impulse noise being efficiently removed,
especially in the cases of heavy noise corruptions (noise density
greater than 70%). Experimental results show that the proposed
method performs much better than other existing denoising tech-
niques in objective measures and visual quality.