An evaluation of contrast enhancement techniques for mammographic breast masses.

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.     

An adaptive Kalman filter for ECG signal enhancement

The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.     

An infinite impulse response lattice filter for adaptive line enhancement

The least-squares lattice filter is used to implement an infinite-impulse-response adaptive filter for the enhancement of narrowband signals in the presence of white or finitely correlated noise. The advantages of infinite impulse response filters for adaptive line enhancement are discussed. The performance of the proposed technique is illustrated by simulation results.     

An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement

Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies. Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest. Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing. They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured. The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.     

FIR自适应滤波的语音增强算法

提出一种基于线性预测FIR自适应滤波的语音增强算法,该算法可实时过滤被噪声污染的语音信号,提高信噪比,从而提高语音识别系统的识别率。仿真结果证明该算法具有较好的降噪效果。     

An optimal adaptive thresholding based sub-histogram equalization for brightness preserving image contrast enhancement

Multidimensional Systems and Signal Processing - In this paper, a new adaptive thresholding based sub-histogram equalization (ATSHE) scheme is proposed for contrast enhancement and brightness...     

Image contrast enhancement using geometric mean filter

Histogram equalization HE is one of the most popular methods for image contrast enhancement. However, the intensity of the input image plays an important role on its performance. In particular, HE fails to enhance images with a dominant color. Therefore, several techniques were proposed to tackle this problem. Some are built for brightness preservation, and others aim to maximize the preservation of structural information. In this paper, we propose an efficient HE enhancement technique that is not only addresses brightness preservation but also both edge and structural information preservation. The proposed technique investigates the geometric mean filter for smoothing the peaks in the histogram before applying the HE. To support our claims, a set of experiments were conducted. Remarkably, through qualitative and quantitative evaluations, results demonstrate that the performance of the proposed method, when compared with a set of other state-of-the-art methods including HE, CLAHE, Log-Power, BPDFHE and DWT–SVD, shows a significant improvement especially in terms of structural and edge preservation.     

An adaptive resonant filter

A solid-state adaptive (analog storage) device with stable electrical characteristics is described and demonstrated. The device is a resonant bandpass electronic filter with adaptable voltage gain; that is, the voltage gain-frequency transfer characteristic can be "set" to different values of attenuation by the application of an adapt signal and will retain that "setting" after the adapt signal has been removed. Ferroelectric materials are used as the dielectric in a filter structure composed of two capacitors bonded together so that resonant mechanical vibrations established in one (the input resonator) are coupled to the other (the output resonator). Converse and direct piezoelectric effects generate the mechanical vibrations and the output voltages, respectively. Ferroelectric effects in either capacitor provide the analog storage capabilities. The acoustical coupling mechanism employed in the device design results in electrically stable device characteristics. Previous ferroelectric adaptive devices used unstable field effect coupling mechanisms which led to unacceptable device performance. Experimental adaptive resonant filters fabricated with ceramic lead zirconate-lead titanate material compositions are discussed. These filters have electronic Q values near 100 at resonant frequencies in the range 10²to 10⁷Hz. The voltage gain-frequency characteristic has a maximum value at resonance of about 0 to +10 dB. Application of a voltage adapt pulse (100 to 300 volts) of low energy (mJ) to either side of the filter can adapt the entire gain characteristic by any value between 0 and about -60 dB within an arbitrary switching time (limited to a practical range of roughly 10⁺³to 10^-4seconds) as determined by the pulse amplitude. Voltage gain settings are electrically stable and can be reproduced by the same or an equivalent sequence of adapting pulses.     

An adaptive morphological filter for image processing

Novel types of opening operator (NOP) and closing operator (NCP) are proposed. An adaptive morphological filter is then constructed on the basis of the NOP and NCP. The filter can remove any details consisting of fewer pixels than a given number N, while preserving the other details. Efficient algorithms are also developed for the implementation of the NOP and NCP.     

An adaptive recursive LMS filter

An adaptive, recursive, least mean-square-digital filter is heuristically derived that has the computational simplicity of existing transversal adaptive filters, with the additional capability of producing poles in the filter transfer function. Simulation results are presented to demonstrate its capability.     

Wireless capsule endoscopy images enhancement via adaptive contrast diffusion

Wireless capsule endoscopy (WCE) has been widely applied to diagnose diseases in human digestive tract due to its advantage that it can directly view the entire small intestine for the first time. However, many WCE images are rather dark, which challenge to analysis and diagnosis exerted by a clinician. To overcome this shortcoming so as to assist physicians, especially computer aided detection, we propose an adaptive contrast diffusion to enhance WCE images. Based on local analysis of WCE images, we put forward a new idea of contrast diffusion. Then, we employ contrast diffusion to enhance WCE images with an adaptive choice of the conductance parameter, which plays an important role in diffusion. Extensive experiments demonstrate that this new method exhibits promising performance of enhancement for WCE images, leading into a better visualization as well as an improved classification performance of WCE images using computerized methods.     

A novel adaptive approach to fingerprint enhancement filter design

A novel procedure for fingerprint enhancement filter design is described. Fingerprints are best used as unique and invariant identifiers of individuals. Identification of fingerprint images is based on matching the features obtained from a query image against those stored in a database. Poor quality of fingerprint images makes serious problems in the performance of subsequent matching process. The main contribution of this work is to quantify and justify the functional relationship between image features and filter parameters. In this work, the enhancement process is adapted to the input image characteristics to improve its efficiency. Experimental results show the superiority of the proposed enhancement algorithm compared to the best fingerprint enhancement procedures reported in the literature.     

An adaptive filter for smoothing noisy radar images

Radar images are corrupted by multiplicative noise due to fading. This paper presents an algorithm for smoothing noisy radar images. The algorithm is easily implemented in the spatial domain and is computationally very efficient. By adapting the filter parameters to local statistics, it is shown that the filter preserves edges.     

An adaptive order-statistic noise filter for gamma-corrected imagesequences

Original video signals are often corrupted by a certain amount of noise originating from the camera electronics. As a result of the gamma correction in cameras, the observed noise is signal dependent. We present a spatio-temporal order-statistic (OS) noise filter that takes into account the gamma correction in the camera. The calculation of the filter coefficients requires higher-order order-statistics (HOOS) of the noise process. We make use of a range test (RT) to determine locally from which neighboring signal values an estimate should be formed. The noise filter that we arrive at is adaptive and computationally efficient.     

An adaptive temporal-spatial filter for MPEG decoded video signals

A new adaptive post-processing algorithm for the MPEG decoded video sequences is proposed. We use a motion compensated averaging filter to reduce the noises in the temporal domain and an adaptive spatial filter to remove noise in the spatial domain and preserve the edge of different orientations in the image. A MPEG decoded video sequence called table tennis is processed by our proposed filter. The post-processed video sequence shows that its image quality is improved, especially of the moving objects.     

An adaptive speckle suppression filter for medical ultrasonic imaging

An adaptive smoothing technique for speckle suppression in medical B-scan ultrasonic imaging is presented. The technique is based on filtering with appropriately shaped and sized local kernels. For each image pixel, a filtering kernel, which fits to the local homogeneous region containing the processed pixel, is obtained through a local statistics based region growing technique. The performance of the proposed filter has been tested on the phantom and tissue images. The results show that the filter effectively reduces the speckle while preserving the resolvable details. The simulation results are presented in a comparative way with two existing speckle suppression methods.     

基于自适应卡尔曼的时域增强算法研究

在图像序列进行压缩感知重构的过程中,基于运动补偿的分块压缩感知重建算法利用了帧间残差图像的稀疏特性,有效提高了重构视频的质量。但该算法仅在空域对图像进行了维纳滤波,帧间存在抖动现象,视频主观质量较差。文章将自适应卡尔曼滤波算法应用到分块压缩感知重建算法的重建过程中,可以有效地去除视频帧间的噪声,使得图像的主观质量得到了改善。     

一种三阶Volterra自适应滤波算法

采用更高阶Volterra滤波器更好地逼近非线性系统时,Volterra自适应滤波算法的计算复杂度呈幂级数增加。针对此问题,本文提出了一种在α稳定分布噪声背景下基于离散余弦变换(DCT)的三阶Volterra滤波算法。首先将Volterra滤波器的三次项权系数矩阵分解成一组二次项权系数矩阵;然后利用正交变换,将二次项权系数矩阵变换成对角矩阵,从而大大减少了权系数个数,有效降低了算法的计算复杂度;最后将Volterra自适应滤波器输出表示成线性滤波器输出形式,并由此得到权系数自适应调整算法。系统辨识的仿真结果表明,本算法在α稳定分布噪声背景下具有优越的性能。     

An adaptive analog continuous-time CMOS biquadratic filter

An adaptive analog continuous-time biquadratic filter is realized in a 2-μm digital CMOS process for operation at 300 kHz. The biquad implements the notch, bandpass and low-pass transfer functions. The only parameter adapted is the resonant frequency of the biquad, which is identical to the notch frequency and the bandpass center frequency. The update method is based on a least-means-square algorithm which adapts the notch frequency to minimize the power at the notch filter output. The actual update is modified to reduce the circuit complexity to one biquad and one correlator. When the filter is tracking a sinusoid, this update generates a ripple-free gradient that decreases tracking error. Applications include phase-frequency detectors, FM demodulators (linear and frequency shift keying), clock extractors, and frequency acquisition aids for phase-locked loops and Costas loops. Measured results from experimental prototypes are presented. Nonidealities of an all-analog implementation are discussed, along with suggestions to improve performance