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The key step in reduced-rank noise reduction algorithms is to approximate a matrix by another one with lower rank, typically by truncating a singular value decomposition (SVD). We give an explicit and closed-form derivation of the filter properties of the rank reduction operation and interpret this operation in the frequency domain by showing that the reduced-rank output signal is identical to that...
Positron emission tomography (PET) is capable of capturing the functional information. A major limitation for PET imaging is the low spatial resolution, leading to partial volume effects (PVE). PVE introduces significant bias to the image quantification, causing compromised measurement for uptake regions, especially smaller ones. For quantitative PET, accurate uptake values are critical for diagnostic...
In this paper, we present a brushlet-based block matching 3D (BM3D) method to collaboratively denoise ultrasound images. Through dividing image into multiple blocks, we group them based on similarity. Then, grouped blocks sharing similarity form a 3D image volume. For each volume, brushlet thresholding is applied to remove noise in the frequency domain. Upon completion of individual filtering, the...
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction...
In this paper, a method of adaptive noise suppression combining spatially robust fixed beamforming and the TRINICON blind source separation algorithm is presented. A multichannel sensor array is first processed using complementary fixed beamformers into maximum and minimum SINR channels. The channels form the inputs to a single 2×2 second-order statistics TRINICON-BSS system which adaptively compensates...
Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. To reduce noise, the non-local means (NLM) filter has been shown to yield state-of-the-art denoising performance. However, NLM relies heavily on the existence of recurring structural patterns and this condition might not always be...
Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary...
In this paper, compressed sensing (CS) is investigated as a denoising tool in bioimaging. Multiple reconstructions at low sampling rates are combined to generate high quality denoised images using total-variation spar-sity constraints. The validity of the proposed method is first assessed on a synthetic image with a known ground truth and then applied to real biological images.
This paper presents a method to enhance a speech signal disturbed by wind noise. The wind noise is generated by turbulences in an air stream close to the microphone which picks up the desired speech signal. As the majority of speech enhancement algorithms works in the frequency domain, the short term power spectrum (STPS) of the unwanted noise must be estimated to reduce the wind noise. Conventional...
In industrial noise environments, the use of assistive listening headsets is a means to provide adequate access to voice communication while wearing hearing protection. This paper presents a performance evaluation and comparison of two different methods to provide the binaural speech enhancement in real industrial noise scenarios. The investigated binaural methods based on differential beamforming...
Images and videos are often captured in poor light conditions, resulting in low-contrast images that are corrupted by acquisition noise. To recreate a high-quality image for visual observation, the captured image must be denoised and contrastenhanced. Conventional methods perform these two tasks in two separate stages: an image is first denoised, followed by an enhancement procedure. In this paper,...
This paper proposes a noise reduction method for screen content coding using HEVC. The proposed method focuses on that the histograms of pixel values for screen content have distinct and sparse peaks respectively. This property is often degraded by the coding distortion, which arises some small peaks around the original peak. This paper presents a method for removing the small peaks derived from the...
In this paper, a very efficient image denoising scheme, which is called nonlocal means based on bidirectional principal component analysis, is proposed. Unlike conventional principal component analysis (PCA) based methods, which stretch a 2D matrix into a 1D vector and ignores the relations between different rows or columns, we adopt the technique of bidirectional PCA (BDPCA), which preserves the...
In this paper, we propose a new image denoising scheme that is an integration of a content-adaptive guided filter and a collaborative Wiener filter. The proposed scheme consists of two steps. First a content-adaptive guided filter, which smoothes image based on spatial similarity within a local window, is applied. The content-adaptive guided filter can efficiently preserve edges while smoothing noise...
Baseline nonlocal means denoising scheme may be improved by incorporating more adaptivity, like locally varying filtering window, smoothing constants and patch size or shape. In this paper, we presents a novel adaptive nonlocal means filtering scheme, the key idea of which is that before computing the similarity between two pixels, active matching is performed to determine optimally matched patch...
Effective use of feature set and selection of a suitable classification method are significant for improving classification accuracy. However, mammogram images classification is affected by many factors such as additive gaussian noise, low contrast and artifacts. Therefore, the aim of this paper is to observe the impact of presence /absence of noise on the quality and classification accuracy of mammogram...
Most of the cardiac disorders are diagnosed by analysis of electrocardiogram (ECG) of the subject. Noise sources in ECG can either be cardiac or extra cardiac, resulting in the distribution of artifacts throughout the original signal. Non-ideal conditions such as electromagnetic interference caused by power cables of the monitoring equipment and muscle or electrode movements corrupt the ECG. This...
In this paper we study the estimation of changing trends in time-series using ℓ1 trend filtering. This method generalizes 1D Total Variation (TV) denoising for detection of step changes in means to detecting changes in trends, and it relies on a convex optimization problem for which there are very efficient numerical algorithms. It is known that TV denoising suffers from the so-called stair-case effect,...
The combination of noise and reverberation make listening conditions difficult for cochlear implant (CI) users. The perceptual effect of reverberation was evaluated via speech intelligibility tests with CI users. A fixed directional microphone, an adaptive directional microphone and a beamformer post-filter were evaluated. Reverberation was varied by changing the target and noise distance and by simulating...
Speech signal is often contaminated by both room reverberation and ambient noise. In this contribution, we propose a nested generalized sidelobe canceller (GSC) beamforming structure, comprising an inner and an outer GSC beamformers (BFs), that decouple the speech dereverberation and the noise reduction operations. The BFs are implemented in the short-time Fourier transform (STFT) domain. Two alternative...
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