The deficiency of the existing level-set-based denoising techniques is that they are sensitive to noise. This is due to that fact that the curvature and gradient measurements in the partial differential equation are very sensitive to noise, and the denoising performance is affected. This work proposes to perform the level-set-based curve evolution on the dyadic wavelet transform domain. The main advantage is that in the dyadic wavelet transform domain, noise has less influence on curvature and gradient measurements as the scale increases. Thus, the edge indicator function value can be directly calculated from the dyadic wavelet coefficients rather than from an external force field by convolving the noisy image with a Gaussian filter. For further reducing the noise at the finest scale where noise is dominant, minimum mean-squared-error (MMSE)-based filtering is performed as the first pass of denoising, followed by performing the level-set curve evolution as the second pass of further denoising and enhancement. Experimental results demonstrate that the proposed algorithm generates state of the art denoising results.
In magnetic resonance (MR) imaging, there is a tradeoff between the spatial resolution, temporal resolution and signal to noise ratio (SNR). MR images usually suffer from low SNR and low resolutions. In order to make it practical for MR imaging with higher resolutions as well as sufficient SNR, it is necessary to reduce noise efficiently while preserving important image features. In this paper, we propose to use the wavelet-based multiscale level-set curve evolution algorithm to reduce noise for MR imaging. Experimental results demonstrate that this denoising algorithm can significantly improve the SNR and contrast to noise ratio (CNR) for MR images while preserving edges with good visual quality. The denoising results indicate that in MR imaging applications, we can almost doubly improve the temporal resolution or improve the spatial resolution while achieving sufficient SNR, CNR, and satisfactory image quality.
Magnetic resonance (MR) images acquired with a high temporal resolution or high spatial resolution are usually with a penalty of low signal to noise ratio (SNR). It is necessary to remove the noise artifacts with important image features such as edges preserved. In this paper, we propose to use the improved wavelet-based multiscale anisotropic diffusion algorithm for MR imaging. Experimental results demonstrate that this denoising algorithm can significantly improve the SNR for MR images while preserving edges with good visual quality. The denoising results indicate that in MR imaging applications, we can almost doubly improve the temporal resolution or improve the spatial resolution while achieving high SNR and acceptable image quality.