Denoising Capabilities Research Articles

A Systems Approach for Data Compression and Latency Reduction in Cortically Controlled Brain Machine Interfaces

This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

Statistical Model-Based VAD Algorithm with Wavelet Transform

This paper presents a new statistical model-based voice activity detection (VAD) algorithm in the wavelet domain to improve the performance in non-stationary environments. Due to the efficient time-frequency localization and the multi-resolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non-stationary signals such as speech. To utilize the fact that the wavelet packet is very efficient approximation of discrete Fourier transform and has built-in de-noising capability, we first apply wavelet packet decomposition to effectively localize the energy in frequency space, use spectral subtraction, and employ matched filtering to enhance the SNR. Since the conventional wavelet-based spectral subtraction eliminates the low-power speech signal in onset and offset regions and generates musical noise, we derive an improved multi-band spectral subtraction. On the other hand, noticing that fixed threshold cannot follow fluctuations of time varying noise power and the inability to adapt to a time-varying environment severely limits the VAD performance, we propose a statistical model-based VAD algorithm in wavelet domain with an adaptive threshold. We perform extensive computer simulations and compare with the conventional algorithms to demonstrate performance improvement of the proposed algorithm under various noise environments.

Accurate optical flow in noisy image sequences using flow adapted anisotropic diffusion

In this paper, we combine 3D anisotropic diffusion and motion estimation for image denoising and improvement of motion estimation. We compare different continuous isotropic nonlinear and anisotropic diffusion processes, which can be found in literature, with a process especially designed for image sequence denoising for motion estimation. All of these processes initially improve motion estimation due to reduction of noise and high frequencies. But while all the well known processes rapidly destroy or hallucinate motion information, the process brought forward here shows considerably less information loss or violation even at motion boundaries. We show the superior behavior of this process. Further we compare the performance of a standard finite difference diffusion scheme with several schemes using derivative filters optimized for rotation invariance. Using the discrete scheme with least smoothing artifacts we demonstrate the denoising capabilities of this approach. We exploit the motion estimation to derive an automatic stopping criterion.

A nonlinear and non-iterative noise reduction technique for medical images: concept and methods comparison

Filtering is a preliminary process in many medical image processing applications. It is aiming at reducing noise in images. Any post-processing tasks may benefit from the reduction of noise. In this paper, a method for edge-preserving smoothing of 2D and 3D medical images is described. The proposed method uses a narrow spatial window and takes only a single iteration to deinoise an image. It integrates geometric, photometric and local structural similarities to achieve nonlinear noise reduction. We have applied this novel method to medical images and compared its denoising capability with other noise reduction techniques. The experimental results have shown that our method is capable of reducing severe noise, and is an adequate preprocess to improving the quality of segmentation and facilitating the feature extraction process.

Noisy speech recognition using de-noised multiresolution analysis acoustic features.

This paper describes a novel application of multiresolution analysis (MRA) in extracting acoustic features that possess de-noising capability for robust speech recognition. The MRA algorithm is used to construct a mel-scaled wavelet packet filter-bank, from which subband powers are computed as the feature parameters for speech recognition. Wiener filtering is applied to a few selected subbands at some intermediate stages of decomposition. For high-frequency bands, Wiener filters are designed based on a reduced fraction of the estimated noise power, making the consonant features much more prominent and contrastive. The proposed method is evaluated in phone recognition experiments with the TIMIT database. In the presence of stationary white noise at 10-dB SNR, the de-noised MRA features attain a phone recognition rate of 32%. There is a noticeable improvement compared with the accuracy of 29% and 20% attained by the commonly used mel-frequency cepstral coefficients (MFCC) with and without cepstral mean normalization (CMN), respectively. The effectiveness of the MRA features is also verified by the fact that they exhibit smaller distortion from clean speech.

Nonlinear inverse scale space methods

In this paper we generalize the iterated refinement method, introduced by the authors in a recent work, to a time-continuous inverse scale-space formulation. The iterated refinement procedure yields a sequence of convex variational problems, evolving toward the noisy image. The inverse scale space method arises as a limit for a penalization parameter tending to zero, while the number of iteration steps tends to infinity. For the limiting flow, similar properties as for the iterated refinement procedure hold. Specifically, when a discrepancy principle is used as the stopping criterion, the error between the reconstruction and the noise-free image decreases until termination, even if only the noisy image is available and a bound on the variance of the noise is known. The inverse flow is computed directly for one-dimensional signals, yielding high quality restorations. In higher spatial dimensions, we introduce a relaxation technique using two evolution equations. These equations allow fast, accurate, efficient and straightforward implementation. We investigate the properties of these new types of flows and show their excellent denoising capabilities, wherein noise can be well removed with minimal loss of contrast of larger objects.