One critical issue in the context of image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image conspicuity and to improve the performances of all the processings needed for quantitative imaging analysis. The method proposed in this paper is based on an optimized version of the Non Local (NL) Means a...

The Non-Local Means (NLM) method of denoising has received considerable attention in the image processing community due to its performance, despite its simplicity. In this paper, we show that NLM is a zero-th order kernel regression method, with a very specific choice of kernel. As such, it can be generalized. The original method of NLM, we show, implicitly assumes local constancy of the underl...

To denoise a reference patch, the Non-Local-Means denoising filter processes a set of neighbor patches. Few Nearest Neighbors (NN) are used to limit the computational burden of the algorithm. Here here we show analytically that the NN approach introduces a bias in the denoised patch, and we propose a different neighbors’ collection criterion, named Statistical NN (SNN), to alleviate this issue....

Clinical MRI data is normally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. For this reason, de-noising methods are often applied to increase the SNR and improve image quality. Most of these methods work on single channel images by correcting each grey level using an implicit model of the surrounding region, but with...

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure i...

Electrocardiogram (ECG) is an important biomedical signal for analyzing heart activity. Analysis of ECG becomes difficult if noise is augmented with the signal during acquisition. During recent years, several denoising techniques were analyzed within the field of signal processing. In this paper, non local means (NLM) filtering technique is explored for denoising the ECG signal. The noisy ECG s...

OBJECTIVE Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The obje...

In this work we comment the results presented in [1] regarding a random sampling approach of the Non Local Means (NLM) image denoising filter with respect to computational cost and denoising performance. We will show that although the approach is novel and mathematically revealing, the computation cost of the approach is higher, and the PSNR lower, compared to the classical version. Furthermore...

Image resolution in 4D-CT is a crucial bottleneck that needs to be overcome for improved dose planning in radiotherapy for lung cancer. In this paper, we propose a novel patch-based algorithm to enhance the image quality of 4D-CT data. Our premise is that anatomical information missing in one phase can be recovered from complementary information embedded in other phases. We employ a patch-based...

In this paper, a novel improvement idea in image restoration by Non-Local means (NL-means) filter is proposed. Most of special image denoising techniques require a region size called neighborhood window for denoising, window-size selection is usually done by visual inspection based upon experience or hit and trial. It is shown in this paper that varying the window-size for each pixel according ...