Contrast enhancement based denoising method in diffusion tensor imaging

This paper applies the nonlocal mean (NLM) method to denoise the simulated and real electroencephalograph signals. As a patch-based method, the NLM method calculates the weighted sum of a patch. The weight of each point is determined by the similarity between the points of the own patch and its neighbor. Based on the weighted sum, the noise is filtered out. In this study, the NLM denoising method is applied to signals with additive Gaussian white noise, spiking noise and specific frequency noise and the results are compared with that of the popular sym8 and db16 Wavelet threshold denoising (WTD) methods. The outcomes show that the NLM on average achieves 2.70 dB increase in improved signal to noise ratio (SNRimp) and 0.37 % drop in improved percentage distortion ratio compared with WTD. The moving adaptive shape patches-NLM performs better than the original NLM when the signals change dramatically. In addition, the performance of combined NLMWTD denoising method is also better than original WTD method (0.50-4.89 dB higher in SNRimp), especially, when the signal quality is poor.

The performance of existing deep convolutional networks is limited when encountering images with different noise levels. In this study, a denoising method with state-of-the-art performance that combines a deep convolutional network with the traditional nonlocal mean denoising method is proposed. The noisy image is first denoised using the nonlocal mean method. Then, the denoised image is input into the proposed dense hybrid convolutional network to be trained, producing a clean image with clear details. The dense hybrid convolutional network comprises three parts: a feature-extracting noise-suppressing module that extracts abstract features from denoised images and suppresses the residual noise by interval convolution; a feature-learning module used for training blurred edges and textures; and a magnifying module that uses deconvolution to restore the feature maps to the original size and reduce the noise again. In contrast to existing denoising algorithms, the method has two desirable properties: 1) it can restore edges and textures clearly while removing the noise; 2) it effectively deals with noise of unknown levels (i.e. blind denoising) with a single network model. The conducted experiments show that the proposed method achieves superior performance compared to those of state-of-the-art denoising methods.

Purpose:Breast cancer can be cured if diagnosed early, with digital mammography which is one of the most effective imaging modalities for early detection. However mammogram images often come with low contrast, high background noises and artifacts, making diagnosis difficult. The purpose of this research is to preprocess mammogram images to improve results with a computer aided diagnosis system. The focus is on three preprocessing methods: a breast border segmentation method; a contrast enhancement method; and a pectoral muscle removal method.Methods:The proposed breast border extraction method employs a threshold based segmentation technique along with a combination of morphological operations. The contrast enhancement method presented here is divided into two phages. In phase I, a bi-level histogram modification technique is applied to enhance the image globally and in phase II a non-linear filter based on local mean and local standard deviation for each pixel is applied to the histogram modified image. The pectoral muscle removal method discussed here is implemented by applying a region growing algorithm.Results:The proposed techniques are tested with the Mini MIAS dataset. The breast border extraction method is applied to 322 images and achieved 98.7% segmentation accuracy. The contrast enhancement method is evaluated based on quantitative measures like measure of enhancement, absolute mean brightness error, combined enhancement measure and discrete entropy. The proposed contrast enhancement method when applied to 14 images with different types of masses, the quantitative measures showed an optimum level of contrast enhancement compared to other enhancement methods with preservation of local detail. Removal of the pectoral muscle from MLO mammogram images reduced the search region while identifying abnormalities like masses and calcification.Conclusions:The preprocessing steps proposed here show promising results in terms of both qualitative and quantitative analysis.

A novel Non-local means image denoising method based on grey theory

The paper presents a novel three-stage algorithm for very-low-light video denoising and enhancement. The proposed technique invokes twice, in the first and the third stages, the well-known Non-Local Means (NLM) method for spatial and temporal denoising: it is well adapted to the application, leading to the definition of a novel NLM tool. The intermediate stage performs a custom tone adjustment specifically aimed at enlarging the dynamic range of very dark videos. The overall approach transforms very dark videos into more watchable ones, effectively reduces very high noise, and all in all, produces high quality restored image sequences outperforming the recent state-of-the-art results. Additionally, the first and third stages can be combined as a two-step filtering scheme for normal-light videos: the novel denoising solution achieves a heavy noise removal, while reducing motion blur artifacts and preserving image details.

Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations.

Denoising is an important step to improve image quality and to increase the performance of image analysis. However, conventional partial differential equation based image denoising methods, especially total variation functional minimization techniques, do not work very well on biomedical images such as magnetic resonance images (MRI), ultrasound, and X-ray images. These images present small structures with signals barely detectable above the noise level which involve more complex noise and unclear edges. The recently developed non-local means (NLM) filtering method can treat these types of images better. The standard NLM filter uses the weighted averages of similar patches present in the image. Since the NLM filter is anon-local averaging method, it is very accurate in removing noise but has computational complexity. We develop efficient and optimized NLM based methods and their associate numerical algorithms. The new methods are still accurate enough and moreeffi-cient than the original NLM filter. Numerical results show that the new methods compare favorably to the conventional denoising methods.

Intelligent vibration signal denoising method based on non-local fully convolutional neural network for rolling bearings

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

This paper presents a method for mammographic image denoising and contrast enhancement based on an overcomplete dyadic wavelet transform. The method is aimed at improving the performance of a previous method as well as minimizing user input. The method is based on local non-linear modification of multiscale gradient magnitudes provided by the wavelet transform. Denoising is accomplished by adaptive soft-thresholding and contrast enhancement by a local non-linear gain operator (clipped local range transformation). The processed mammogram is derived by reconstruction from the modified wavelet coefficients. To demonstrate the operation of the algorithm a simulated mammographic phantom with added noise is used. Results of processing a real mammogram are also presented, indicating the potential value of the presented method.

Despeckling of ultrasound images, as a very active topic research in medical image processing, plays an important or even indispensable role in subsequent ultrasound image processing. The non-local total variation (NLTV) method has been widely applied to denoising images corrupted by Gaussian noise, but it cannot provide satisfactory restoration results for ultrasound images corrupted by speckle noise. To address this problem, a novel non-local total variation despeckling method is proposed for speckle reduction. In the proposed method, the non-local gradient is computed on the images restored by the optimized Bayesian non-local means (OBNLM) method and it is introduced into the total variation method to suppress speckle in ultrasound images. Comparisons of the restoration performance are made among the proposed method and such state-of-the-art despeckling methods as the squeeze box filter (SBF), the non-local means (NLM) method and the OBNLM method. The quantitative comparisons based on synthetic speckled images show that the proposed method can provide higher Peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) than compared despeckling methods. The subjective visual comparisons based on synthetic and real ultrasound images demonstrate that the proposed method outperforms other compared algorithms in that it can achieve better performance of noise reduction, artifact avoidance, edge and texture preservation.

Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of sufficient candidates that use to choose the target patches which have complicated textures. In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. The HMM also allows hidden states to connect to one another to capture the dependencies among coefficients in the transform domain. In the practical part, the experimental results present that the proposed algorithm has the ability to show denoised images better than the results of state-of-the-art denoising methods both objectively in peak signal-to-noise ratio and structural similarity and subjectively using visual results, especially when the noise level is high.

Nonlocal means (NLM) image denoising algorithm is not feasible in many applications due to its high computational cost. High computational burden is due to the search of similar patches for each reference patch in the entire image. In this paper, we present a novel technique of preselecting and grouping the similar patches in the form of a dictionary and hence speeding up the computation of NLM denoising method. We build a dictionary only once, with a set of training images of all possible classes of objects, in which patches with similar photometric structures are clustered together. For each noisy patch, similar patches are searched in the global dictionary. In contrast with previous NLM speedup strategies, our dictionary building approach preclassifies similar patches with the same distance measure as used by NLM method. We achieve a substantial reduction in computational time than the original NLM method especially when search window of NLM is large, without much affecting the PSNR. The proposed algorithm is shown to outperform other prefiltering based fast NLM algorithms computationally as well as qualitatively.

Image quality is greatly affected by noise in various applications such as machine vision and computer vision. Noisy images lead to inaccurate results as well as segmentation and enhancement errors, making denoising vital to efficient image processing. Existing denoising methods do not work effectively on both grayscale and color images. Thus an effective denoising method that works well on both image types is proposed. The proposed two-stage method involves: (i) noisy pixel detection and (ii) noisy pixel restoration. In the first stage, the conglomerate method detects the noisy pixel position; in the second stage, the corrupted value of the noisy pixel is restored using a conglomerate method. The proposed method was used to process a variety of grayscale and color images. The result analysis of image denoising was done in two stages: in the first stage, results are carried out using large standard datasets; comparison of these results with those of five other denoising methods showed that the proposed conglomerate denoising method outperformed these existing ones. The second stage of result analysis considers the state-of-the-art methods in comparison with the proposed method. The proposed method outperforms the state-of-the-art methods for images affected with noise ranging from 10% to 90%.

As a noninvasive technique, electrocardiogram (ECG) plays a significant role in the diagnosis of many cardiac diseases. However, ECG signals are usually corrupted with baseline wander and high-frequency noise. Numerous approaches have been proposed to suppress noise in ECG signals such as finite impulse response (FIR) filter, infinite impulse response (IIR) filter and wavelet method. These methods cannot provide sufficient noise suppression or preserve details very well. The recently proposed nonlocal means (NLM) method can overcome this drawback to some extent, but it cannot denoise the ECG signals effectively at high noise corruption due to the adoption of the noisy signals for determining the weight. To address this problem, a robust iterative nonlocal means (INLM) is proposed in this paper. The proposed method uses an iterative strategy to denoise ECG signals, in which the weight is determined by replacing the noisy signals with their denoised versions. Extensive experiments on the simulated and clinical ECG signals demonstrate that the proposed method can remove noise effectively while preserving useful details of ECG signals very well, and it outperforms above-mentioned denoising algorithms in terms of objective metrics such as root-mean-squared-error (RMSE) and signal-to-noise ratio (SNR).