Removal of stripe noise with spatially adaptive unidirectional total variation

Multi-detectors imaging system often suffers from the problem of the stripe noise, which greatly degrades the quality of the resulting images. To better remove stripe noise and preserve the edge and texture information, a robust destriping algorithm with spatially adaptive unidirectional total variation (SAUTV) model is introduced. The spatial information of the striping noise is detected by using the stripe indicator called difference eigenvalue, and a weighted parameter determined by the difference eigenvalue information is added to constrain the regularization strength of the UTV regularization. The proposed algorithm can effectively remove the stripe noise and preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Split Bregman method is utilized to efficiently solve the resulting minimization problem. Comparative results on simulated and real striped images taken with two kinds of imaging systems are reported.     

一种双正则项全变差高光谱图像去噪算法

受传感器特性影响,高光谱图像中的噪声在各个维度都有体现。噪声的存在降低了高光谱图像中信息的有效性,在进行地物分类前必须采用适当的算法对噪声予以去除。文章针对高光谱图像的噪声特性,提出了一种基于全变差的高光谱图像去噪算法。该算法将经典二维图像全变差去噪模型推广至三维形式,提出了采用双正则项及相应的调整参数的目标函数,在三维空间中完成新目标函数的离散化,并采用基于优化-最小化算法的迭代方法对目标函数进行优化与求解。对星载Hyperion成像光谱仪数据的实验表明,适当的设置调整参数,该方法可很好地提高高光谱图像的各波段信噪比、平滑光谱曲线并保留细节特征,其去噪效果优于经典的MNF去噪算法及Savitzky-Golay滤波方法。     

A traffic noise prediction method based on vehicle composition using genetic algorithms

The effect of traffic composition on the noise pollution has been investigated in a small city Erzurum, located in eastern Turkey, where the population is about 400,000. Manual noise measurements and vehicle counts were performed at the four heaviest traffic points in the city for a period of 12 h. Using the information on vehicle composition and the maximum legal noise emissions of each type of vehicles, two prediction models based on genetic algorithms have been developed that can be used as tools for in-city traffic flow redesign. The models have been validated against some of the noise data. Measured traffic noise levels have been compared with predictions using the new models and some existing ones and a relatively good agreement has been obtained between them.     

Morphological impulsive noise removal based on multi scale dual hit-or-miss transforms

Impulsive noise removal is an active research area in optical signal processing. Morphological operators have been tried for impulsive noise removal. However, the performance is not very effective. Dual hit-or-miss transforms could identify the protruding bright or dark pixels like impulsive noise pixels in image, which may be used to construct effective algorithm for impulsive noise removal. In this paper, an algorithm based on the multi scale dual hit-or-miss transforms is demonstrated. Firstly, the positive impulsive noise pixels, which are bright pixels, are identified by multi scale hit-or-miss transform. Then, the negative impulsive noise pixels, which are dark pixels, are identified by multi scale dual hit-or-miss transform. Finally, the identified impulsive noise pixels are removed and replaced by a reasonable value estimated by adaptive median filter. Experimental results show that, because the multi scale dual hit-or-miss transforms could effectively and correctly identify the impulsive noise pixels, the noise pixels are correctly removed and the real image details could be well maintained.     

Demosaicking for multispectral images based on vectorial total variation

Multispectral images (MSIs), which consist of more color components than RGB images, can be used in the field of vegetation analysis and medical imaging. A capturing system with multispectral filter array (MSFA) technology has been researched to shorten the capturing time and reduce the cost. In this system, the mosaicked image captured by the MSFA is demosaicked to reconstruct the MSI. We propose a demosaicking method using vectorial total variation (VTV) regularization for an MSI. This process is regarded as inverse problem of the image observation model. The reconstructed image is estimated by minimizing the VTV as a regularization term under the constraint condition. In the experimental results, the reconstructed image quality obtained using the proposed method is better than that of the conventional approaches in terms of both peak signal-to-noise ratio and structural similarity.     

Nonlinear noise reduction for electrocardiograms

The electrical activity of the heart usually shows dynamical behavior which is neither periodic nor deterministically chaotic: The interbeat intervals seem to contain a random component. Although long term predictions are thus impossible, good predictions can be made for times smaller than one heart cycle. This fact is used in order to suppress measurement errors by a local geometric projection method which was originally developed for chaotic signals. The result constitutes evidence that techniques of time series analysis based on chaos theory can be useful despite the fact that very few natural phenomena have been actually established to be deterministically chaotic. (c) 1996 American Institute of Physics.     

基于总变分的湍流退化噪声图像盲反卷积复原

根据总变分的噪声抑制特性和大气湍流成像过程,建立了基于总变分的大气湍流噪声图像多帧盲反卷积复原最小化模型,以基于共轭梯度数值优化方法的交替迭代算法求解,复原出了观测目标的清晰图像。在计算机上模拟了湍流退化和噪声污染图像。实验结果表明,该复原算法能有效地克服大气湍流和噪声的影响,可复原出清晰的原始目标图像。     

Subspace algorithms for noise reduction in cochlear implants

A single-channel algorithm is proposed for noise reduction in cochlear implants. The proposed algorithm is based on subspace principles and projects the noisy speech vector onto "signal" and "noise" subspaces. An estimate of the clean signal is made by retaining only the components in the signal subspace. The performance of the subspace reduction algorithm is evaluated using 14 subjects wearing the Clarion device. Results indicated that the subspace algorithm produced significant improvements in sentence recognition scores compared to the subjects' daily strategy, at least in stationary noise. Further work is needed to extend the subspace algorithm to nonstationary noise environments.