Multiresolution Non-Local Means Filtering for Image Denoising of MRI
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초록
We propose a new method for the reduction of noise present in the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. Similarly, Wavelet transform (WT) based denoising produces a better noise estimation. The proposed algorithm performs denoising in three functional steps. First, the noisy image is decomposed into multiple subbands by using the wavelet transform. NLM filter is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution NLM filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experiments show that the algorithm can reduce image noise effectively in terms of image quality metrics.
Abstract
We propose a new method for the reduction of noise present in the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. Similarly, Wavelet transform (WT) based denoising produces a better noise estimation. The proposed algorithm performs denoising in three functional steps. First, the noisy image is decomposed into multiple subbands by using the wavelet transform. NLM filter is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution NLM filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experiments show that the algorithm can reduce image noise effectively in terms of image quality metrics.
참고문헌 (11)
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