Adaptive wavelet filters as practical texture amplifiers for early Parkinson’s disease screening from retinal pathology perspective

A wavelet adaptive filter (WAF) for the removal of baseline wandering in ECG signals is described. The WAF consists of two parts. The first part is a wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan-Hoang wavelets. The second part is an adaptive filter that uses the signal of the seventh lowest-frequency band among the wavelet transformed signals as primary input and a constant as reference input. To evaluate the performance of the WAF, two baseline wandering elimination filters are used, a commercial standard filter with a cutoff frequency of 0.5 Hz and a general adaptive filter. The MIT/BIH database and the European ST-T database are used for the evaluation. The WAF performs better in the average power of eliminated noise than the standard filter and adaptive filter. Furthermore, it shows a lower ST-segment distortion than the standard filter and the adaptive filter.

Wavelet analyses of electroanalytical chemistry responses and an adaptive wavelet filter

This work designs a wavelet adaptive filter (WAF) to remove the baseline drift from pulse waveforms. The WAF consists of two parts: the transform algorithm based on discrete Meyer wavelet to decompose the pulse signal into eight frequency bands; the improved adaptive filter that uses the high-frequency components of the pulse signal as reference input and the original pulse waveform added baseline drift as primary input. The WAF is tested on our developed pulse diagnosis apparatus. The results both on simulated and real human pulse signals demonstrate that the proposed WAF outperforms traditional filters not only in removing baseline drift but in preserving the diagnostic information of pulse waveforms.

Vibration-based schemes are founded on the assumption that vibration signals from gearboxes measured using accelerometers reflect their condition accurately. A large number of vibration based techniques are used to make this reflection. They include various spectral analyses such as traditional Fourier transform, short-time Fourier transform, amplitude phase modulation and time synchronous averaging and non-parametric special estimation. Recently, Wavelet Transform (WT) has been proven to be more suitable for analysis of vibration signals, since most of the time-vibration signals have instantaneous impulse trains and exhibit a transient (non-stationary) nature. This paper uses an adaptive wavelet filter, based on the Morlet wavelet, applied on the torsional vibration data measured from a single-stage gearbox with artificially induced cracks in the gear. This is done to extract some parameters and check their diagnostic behavior in an effort to search for those with the most potential and appropriateness for future health monitoring schemes. The results demonstrate that the adaptive wavelet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early tooth cracks. Moreover the influence of crack depth, speed, and load on the wavelet entropy are interduced. Multi-hour tests were conducted and recordings were acquired using torsional vibration monitoring. The transitions in the wavelet entropy values with the recording time were highlighted suggesting critical changes in the operation of the gearbox.

The selection of wavelet scale and filter coefficients is a key technology for wavelet edge detection. In this paper, we propose an algorithm of constructing adaptive spline wavelet filter coefficients based on the genetic algorithm. We detect image edges by employing the obtained filter coefficients, and obtain a good effect of edge extraction by integrating the edge connection algorithm. Finally, experimental results are compared with Canny's operator, and demonstrate the performance of our algorithm

A spatially adaptive two-dimensional wavelet filter is used to reduce speckle noise in time-domain and Fourier-domain optical coherence tomography (OCT) images. Edges can be separated from discontinuities that are due to noise, and noise power can be attenuated in the wavelet domain without significantly compromising image sharpness. A single parameter controls the degree of noise reduction. When this filter is applied to ophthalmic OCT images, signal-to-noise ratio improvements of >7 dB are attained, with a sharpness reduction of <3%.

Images are widely used in engineering. But, some images such as medical ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, we suggest three phases and three denoising filters. In the first phase, the coefficient of variation is computed from the noisy image. Next, fuzzy c-means (FCM) is applied to the coefficients of variation. Applying FCM leads to the fuzzy classification of image regions. Next, the second phase is a hybrid of the three denoising filters. Fast bilateral filter (BF) for homogeneous regions, improved the adaptive wiener filters (AWFs) and wavelet filter that are applied on homogeneous, detail and edge regions, respectively. The proposed improved AWF has been developed from the AWF. In the third phase, the output image is evaluated by the fuzzy logic approach. Thus, with three phases, the proposed method has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.

Wavelet analysis has been widely used in signal de-noising or transient signal detection due to its extraordinary time-frequency representation capability. Traditional approach on the selection of parameters for a adaptive wavelet filter was based on higher order statistics that only present limited statistical information about the bearing fault signals. An adaptive wavelet filter based on the Fractional Lower Order Moment (FLOM) of alpha stable distribution is proposed in this paper. The parameters of the Morlet wavelet filter are optimized based on the principle of maximization of FLOM. The diagnosis results based on the the simulated bearing fault signal with low signal to noise ration (SNR) demonstrated the effectiveness of the proposed approach.

A challenging task in psychophysiology is the extraction of event-related potentials (ERPs) from the background electro-encephalogram. The task is made more difficult by the properties of ERPs, which typically consist of multiple features of variable latency, localised in time and frequency. A novel technique is described for analysis of ERPs, adaptive wavelet filtering (AWF), which is proposed as an alternative to trial averaging. Band-limited detail representations of each trial are obtained using wavelet analysis. The Woody adaptive filter is then used to align trials with respect to the evoked response. In a simulation study, the AWF extracts 39% of higher-frequency signal variance from background noise, compared with less than 1% for standard averaging and the Woody filter. The AWF is applied to a data-set of 448 ERPs, comprising right-finger button presses from eight subjects. Average split-half reliability of the AWF on scales up to 12 Hz was 0.51.

In order to suppress the strong noise, an adaptive morphological wavelet filter algorithm based on the morphological wavelet algorithm is applied in the high precise gravimeter signal processing. The preliminary work of the algorithm is to remove impulse signals of gravimeter signals with morphology filter. Then an adaptive thresh of wavelet transform filter it again. Finally, the reconstructed gravimeter signals without noise is obtained. Results of emulations show that the de-noising performance of the adaptive morphological wavelet filter algorithm are better than those of the classical wavelet filter algorithm.

Despite intensive research being conducted on the topic of adaptive filter design in general, adaptive filter design in the discrete wavelet transform (DWT) domain with specific constraints is still an active research area. We have developed a method for the design of a 2-channel perfect-reconstruction adaptive wavelet filter which is optimized under minimum energy constraints in specific bands. The optimal 2-channel conjugate quadrature filter (CQF) bank has been designed using sequential quadratic programming techniques for nonlinear, nonconvex functions in general, although finding a global minimum is not guaranteed. However, such a filter can be effectively used with a wavelet based image encoder for high fidelity transmission of large image data sets at low bit rates.

Wavelet based compression schemes are the natural choice for the multi-resolution representation of images because of their successive approximation and better decorrelation property. Experiments conducted by compressing images through wavelet filters and integer wavelet transforms suggest that the filter performance indeed is image dependent. It is observed that no wavelet filter outperforms others uniformly while compressing sample images drawn from a large selection. In fact, a detailed analysis of the results reveals that certain wavelets perform better on certain classes of images. A neural network can therefore, be used to categorize the input image into one of these classes. A wavelet-based lossy or lossless coder is then used to compress the image using the most appropriate wavelet filter or integer-transform suitable for that class.

Wavelet-based image coding algorithms lossy or lossless, use a fixed perfect reconstruction (PR) filterbank built into the algorithm for coding and decoding all kinds of images. This generic approach of filter selection and usage may not always give the best compression from the viewpoint of a specific application. However, no systematic study has been done to see if using different wavelet filters for different image types improves the coding performance. To explore this problem, a variety of wavelets are used to compress a variety of images at different compression ratios and the results are reported here. The result, intuitive at best, is that the performance in lossy coders is image dependent and while some wavelet filters perform better than others depending on the image being coded, no specific wavelet filter performs uniformly better than others on all test images. This observation leads to the hypothesis that both for lossy and lossless compression, the "most appropriate" wavelets should be chosen adaptively depending on the statistical nature of image being coded, to achieve better compression.

Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000

Wireless location by spread spectrum communication can be severely corrupted by noise. Narrowband noise is the most effective interference that can make measurement signal undetected. In this paper, an adaptive wavelet filter is presented to suppress single and multiple narrowband interferences with additive white Gaussian noise that interferes with spread spectrum signals. The filter uses combinations of Gaussian wavelets with optimal time-frequency localization and computational efficiency for real-time denoising. The performance of the wavelet filter has been evaluated by experiments with spread spectrum communication system. Experimental results demonstrate that the proposed wavelet filter mitigates the narrowband noise in accordance with the corrupted frequency contents, and improves signal to noise ratio (SNR) for peak detection leading to higher accuracy of timing measurement for real-time wireless location.