New Random Noise Denoising Method for Biomedical Image Processing Applications
Since the inception of digital image processing, noise removal in images has always been a challenge to researchers and experts of the field. Most significant of these noises are the randomly varying impulse noises developed while image is acquired. Hence, the need for methodical denoising method has led to extensive research and development of various innovative methods to remove the random valued impulse noise. For this, a method which detects and filters random valued impulse noise in medical images is employed. The method proposed in this paper uses a decision tree based impulse detector and an edge preserving filter to rebuild noise free images. This method is more efficient than the existing techniques due to its lower complexity. Different gray scale Magnetic Resonance Imaging (MRI) brain images are tested by using this algorithm and have given better Peak Signal to Noise Ratio (PSNR) than the other techniques.
KeywordsImpulse noise Salt-and-pepper noise Random-valued impulse noise Effective noise removal Decision tree Edge preserving filter
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