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.     

一种自适应提升视频图像对比度的算法

面向数字电视视频后处理芯片中提高对比度的需求,针对传统直方图均衡技术偏离原始平均亮度,放大背景噪声的缺点,提出了一种基于改进的直方图均衡的视频图像对比度提升技术.首先改善统计直方图的方法,将得到的累积直方图限制在一定范围内;然后根据平均亮度计算均衡系数;最后根据累积直方图和均衡系数,得到变换函数曲线并进行映射.仿真结果表明,应用该方法处理后的视频图像,在提升图像对比度的同时,既保持了原始图像的平均亮度,又没有增强背景噪声.     

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.     

基于自适应对比度增强的红外小目标检测网络

红外探测系统需要尽早发现目标以便及时拦截,但是红外图像上的小目标检测是一个挑战十足的任务。为了提高检测准确率,提出一种基于自适应对比度增强的红外小目标检测方法。为了利用自注意力机制和卷积各自的优势,设计了一个高效的特征提取网络和一个面向小目标的检测头。同时为了解决实际应用中出现的弱目标,在检测子网络前添加了一个图像预处理子网络,该模块可以自适应地调节图像对比度。在红外空中小目标数据集上的实验表明,提出的方法能达到93.76%的检测精度,与经典的检测方法相比,能够更好地平衡检测精度和召回率,证明了方法的巨大应用潜力。     

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.     

Lifting-based wavelet domain adaptive Wiener filter for image enhancement

A method of applying lifting-based wavelet domain Wiener filter (LBWDMF) in image enhancement is proposed. Lifting schemes have emerged as a powerful method for implementing biorthogonal wavelet filters. They exploit the similarity of the filter coefficients between the low-pass and high-pass filters to provide a higher speed of execution, compared to classical wavelet transforms. LBWDMF not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. The proposed method utilises the multi-scale characteristics of the wavelet transform and the local statistics of each subband. The proposed method transforms an image into the wavelet domain using lifting-based wavelet filters and then applies a Wiener filter in the wavelet domain and finally transforms the result into the spatial domain. When the peak signal-to-noise ratio (PSNR) is low, transforming an image to the lifting-based wavelet domain and applying the Wiener filter in the wavelet domain produces better results than directly applying Wiener filter in spatial domain. In other words each subband is processed independently in the wavelet domain by a Wiener filter. Moreover, in order to validate the effectiveness of the proposed method the result obtained using the proposed method is compared to those using the spatial domain Wiener filter (SDWF) and classical wavelet domain Wiener filter (CWDWF). Experimental results show that the proposed method has better performance over SDWF and CWDWF both visually and in terms of PSNR.     

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.     

A method for detection of signals using frequency-domain adaptive filter

The detection performance of the frequency-domain adaptive filter is compared with the time-domain one. Comparisons are based on the computer digital simulation with a sinusoidal signal and white Gauss noise. The performance of the frequency and time-domain adaptive filters are shown to be the same for an unknown frequency signal, but for the detection of known frequency the frequency-domain adaptive filter length is much shorter to get the same detection probability.     

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.