Medical images are generally of poor contrast and they also get
complex type of noise and blur. The noise also has variability from
one condition to other. So it is very difficult to suggest a robust
method for noise removal which works equally well for different
modalities of medical images. During the denoising process of a
noisy image, it is usually helpful to look at an image at different
resolutions so that important information about both the image and
the noise can emerge easily. If the chosen resolution is too
coarse, fine details will not be visible. On the other hand,
looking too closely at an object can cause surroundings to
disappear, so the noise and the object cannot be distinguished
easily. This is where wavelets can be useful. But unfortunately the
present wavelet based techniques for medical image denoising are
too particular and are useful in particular situations only. Here,
it is important to mention that complex wavelet transform has not
found its deserving place in many applications, and one of the
major challenging tasks taken up in this work is to apply complex
wavelet transform for denoising.
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