22-09-2012, 02:37 PM
Spike noise removal in the scanning laser microscopic image
of diamond abrasive grain using a wavelet transform
1Spike noise removal.pdf (Size: 383.21 KB / Downloads: 21)
Abstract
To remove spike noise in the scanning laser microscopic image of diamond abrasive grain without blurring the sharp
edges, a new smoothing technique that combines a conventional averaging technique with wavelet transforms is proposed.
The diamond abrasive grain image is decomposed into high- and low-frequency subimages using wavelet filters,
and all subimages except the lowest frequency one are synthesized to obtain a high-frequency image, from whose pixel
values spike noise points are extracted. A conventional averaging technique is then applied to the same points in the
original image as the spike noise points in the high-frequency image. The smoothing technique successfully removes
both clustered and unclustered spike noise while preserving the sharp edges. Spike noise is removed without a loss in the
original grain shape. This smoothing technique will surely be effective for other applications.
Introduction
Scanning laser microscopic (SLM) images of
diamond abrasive grains are degraded by spike
noise due to the weak detection of laser reflection
intensity caused by slanting surfaces, hollows, and
so on. Spike noise thus makes it difficult to obtain
accurate information about diamond grains. To
improve the quality of such degraded images, the
noise interference must be removed. In this study,
we propose a smoothing technique that combines
a conventional averaging technique with a 2-D
discrete wavelet transform in order to remove
spike noise in the SLM diamond abrasive image
without the blurring of sharp edges generally
caused by smoothing
Theory
The SLM image of diamond abrasive grain to
be smoothed is decomposed into subbands using
wavelet filters. Then, all subbands except the
lowest spatial frequency subband are synthesized
to obtain the high-frequency image. The high-frequency
image is used to distinguish between noise
and edge components and to extract information
about the magnitude and position of noise. On the
basis of the noise information, pixel points to be
processed are selected and smoothing filters for the
points are determined. In this way, the present
smoothing technique attempts to remove spike
noise from the original SLM diamond grain image
without a loss in edge sharpness.
Discussion
The distinct difference seen in Figs. 6(d) and 8
between the present smoothing technique and the
other ones is attributable to the method of
smoothing. The present technique first applies iterative
smoothing not for all pixels in the image,
but for definitely determined noisy pixels. As a result
of the iterative smoothing for large noise
components, the pixel values, which were mostly
quite low (see the profile curve in Fig. 3(a)), became
progressively higher, because there were still some
signals having high-pixel values in a 5 5 neighborhood
of the noisy points. The values were further
increased with the successive iterations of the
first-step smoothing and approached signal component
values, which could account for the absence
of spike noise in Figs. 3(b) and 6(d). To demonstrate
this by example of 1-D data simulating
clustered spike noise.
Conclusion
To remove spike noise in the SLM image of
diamond abrasive grain without blurring sharp
edges, a new smoothing technique that combines a
conventional averaging technique with a 2-D discrete
wavelet transform was proposed. This technique
proved effective for removing both clustered
and unclustered spike noise while preserving sharp
edges. Because the spike noise was eliminated
without loss of the original shape, good restoration
of the original diamond shape could be
achieved. This smoothing technique will surely be
effective for other applications.