Ultrasound images are affected by the well known speckle phenomenon, that degrades their perceived quality. In recent years, several denoising approaches have been proposed. Among all, those belonging to the non-local (NL) family have shown interesting performance. The main difference among the proposed NL filters is the metric adopted for measuring the similarity between patches. Within this manuscript, a statistical metric based on the ratio between two patches is presented. Compared to other statistical measurements, the proposed one is able to take into account the texture of the patch, to consider a weighting kernel and to limit the computational burden. A comparative analysis with other despeckling filters is presented. The method provided good balance between noise reduction and details preserving both in case of simulated (by means of Field II software) and real (breast tumor) datasets.INDEX TERMS Despeckling, ultrasound imaging, non-local filters, wKSR-NLM.
The topic of denoising magnetic resonance (MR) images is considered in this paper. More in detail, an enhanced Non-Local Means (NLM) filter using the Kolmogorov-Smirnov (KS) distance is proposed. The KS-NLM approach estimates the similarity between image patches by computing the KS distance. To overcome that NLM filters assign the same role to all pixels in patches, that is, not privileging the central one, we propose a new filter, namely the Anisotropic Weighted KS-NLM (Aw KS-NLM), which better deals with central pixels within the patches by, on one hand, including a suitable weighted strategy and, on the other, by performing a local anisotropy analysis. The Aw KS-NLM has been compared to other existing non-local Means (NLM) methodologies in both MRI simulated and real datasets. The results provide excellent noise reduction and image-detail preservation.
Speckle noise is presented as an inherent dilemma that affects the image processing field, and in particular synthetic aperture radar images. In order to mitigate the adverse effects caused by this phenomenon, several approaches have been introduced in the scientific community during the last three decades including spatial-based and non-local filtering approaches. However, these proposed techniques suffer from some limitations. In fact, it is very difficult to find an approach that is able, on the one hand, to perform well in terms of noise reduction and image detail preservation and, on the other hand, provide a filtering output solution without high computational complexity and within a short processing time. In this paper, we aim to evaluate the performance of a newly-developed despeckling algorithm, presented as an enhancement of the classical Wiener filter and properly designed to work with a Graphics Processing Unit (GPU). The algorithm is tested on both a simulated framework and real Sentinel-1 SAR data. The results, obtained in comparison with other filters, are interesting and promising. Indeed, the proposed method turns out to be a useful filtering instrument in the case of large images by performing the processing within a limited time and ensuring good speckle noise reduction with a considerable image detail preservation.
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