Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter

The Magnetic Resonance Imaging (MRI) technologyused in clinical diagnosis demands high Peak Signal-to-Noise ratio(PSNR) and improved resolution for accurate analysis and treatmentmonitoring. However, MRI data is often corrupted by random noisewhich degrades the quality of Magnetic Resonance (MR) images.Denoising is a paramount challenge as removing noise causesreduction in the fine details of MRI images. We have developed anovel algorithm which employs Principal Component Analysis(PCA) decomposition and Wiener filtering. We have proposed a twostage approach. In first stage, non-local PCA thresholding is appliedon noisy image and second stage uses Wiener filter over this filteredimage. Our algorithm is implemented using MATLAB andperformance is measured via PSNR. The proposed approach hasalso been compared with related state-of-art methods. Moreover, wepresent both qualitative and quantitative results which prove thatproposed algorithm gives superior denoising performance

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Nonlocal Means-Based Denoising for Medical Images

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed

Adaptive Edge-guided Block-matching and 3D filtering (BM3D) Image Denoising Algorithm

Image denoising is a well studied field, yet reducing noise from images is still a valid challenge. Recently proposed Block-matching and 3D filtering (BM3D) is the current state of the art algorithm for denoising images corrupted by Additive White Gaussian noise (AWGN). Though BM3D outperforms all existing methods for AWGN denoising, still its performance decreases as the noise level increases in images, since it is harder to find proper match for reference blocks in the presence of highly corrupted pixel values. It also blurs sharp edges and textures. To overcome these problems we proposed an edge guided BM3D with selective pixel restoration. For higher noise levels it is possible to detect noisy pixels form its neighborhoods gray level statistics. We exploited this property to reduce noise as much as possible by applying a pre-filter. We also introduced an edge guided pixel restoration process in the hard-thresholding step of BM3D to restore the sharpness of edges and textures. Experimental results confirm that our proposed method is competitive and outperforms the state of the art BM3D in all considered subjective and objective quality measurements, particularly in preserving edges, textures and image contrast