基于多尺度脊波字典的图像去噪算法

基于人眼视觉系统特性,根据图像几何结构特征,提出一种多尺度脊波字典的构造方法。构造出的多尺度脊波字典具有多分辨率、多尺度、各向异性和多方向等特性,能够更为稀疏地表示图像,从而得到一种基于多尺度脊波字典的稀疏性约束图像去噪算法。实验结果表明,该去噪算法能有效去除噪声,更好地保留图像的边缘等细节。     

Patch based image denoising using the finite ridgelet transform for less artifacts

Patch based denoising methods have proved to lead to state-of-the-art results. However, in contrast with intensive pursuing of higher peak signal to noise ratio (PSNR), less attention is paid to visual quality improvement of denoised images. In this paper, we first compare the denoising performance in edge and smooth regions. Results reveal that edge regions are the main source for potential performance improvement. This motivates us to investigate the use of the finite ridgelet transform as a local transform for better preservation of directional singularities. A two stage denoising algorithm is then proposed to improve the representation of detail structures. Experimental results in denoising images which only contain white noise show that the proposed algorithm consistently outperforms other methods in terms of PSNR and Structural SIMilarity index. Denoised images by the proposed method also demonstrate good visual quality with the least artifacts and fake structures in experiments on natural images.     

基于MG-HMT模型的正交有限脊波域图像分割

针对图像分割中的过分割问题,提出了一种基于图像混合高斯-隐Markov树(Mixture Gaussian-hidden markov tree,MG-HMT)模型的正交有限脊波分析的图像分割算法.正交有限脊波变换处理信息时具有检测信号线奇异的能力,在图像分割中为准确定位信息的边缘、轮廓提供了有力的支持.其次,对图像的...     

Digital ridgelet reconstruction based on local dual frame

Candes and Donoho[1] introduced the concept of the ridgelet transform in 1998. Es- sentially, they applied the Radon transform to the original image, and then used the wavelet analysis in the Radon domain. Such system is very good at representing the smooth functions with line singularities. Thus it has great potentialities in the image compression, denoising, enhancement and other application cases. In the digital imple- mentation respect, Do[2] used the finite ridgelet transform to the ima…     

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.     

A 4-quadrant curvelet transform for denoising digital images

The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.     

一种基于脊波变换的数字水印算法

该文针对脊波变换能有效表示二维图像线性奇异性的特点,提出了一种脊波域鲁棒数字水印算法。对图像进行分块有限脊波变换,有选择地在脊波域最大能量方向上的中频系数中嵌入水印,采用奇异值分解方法提取水印提高了检测性能。实验结果表明,算法对噪声、压缩、不规则剪切、亮度、对比度等攻击有较强的鲁棒性。     

基于粒子群脊波神经网络的飞机目标识别

针对传统的基于梯度的脊波神经网络训练算法存在效率低、网络规模大、后期容易震荡等缺点,提出了一种粒子群网络训练算法,网络结构采取逐步递增隐层神经元的方法加以确定,对粒子群个体参数取不同的位置边界,并对粒子飞行速度进行限制,在合理设定粒子群算法各参数值的情况下实现了网络快速而有效的训练.仿真试验将该网络用于6类空中飞机目标的识别,并与传统的识别手段进行了比较,结果表明粒子群算法训练的脊波神经网络具有规模小、学习和泛化能力强、网络可控性好等优点.     

Adaptive detection of weft‐knitted fabric defects based on machine vision system

This paper describes a machine vision system for the detection of weft‐knitted fabric defects based on an adaptive pulse‐coupled neural network (PCNN) and Ridgelet transform. In order to classify defects according to their different texture features, two methods are implemented: an improved PCNN method to segment the defects such as hole and dropped stitch from background image and a Ridgelet transform method based on wavelet analysis to identify the defect such as course mark. In implementing the PCNN model, necessary parameters of PCNN model such as linking coefficient, connection weight, and iteration number are automatically calculated in accordance with the spatial distance of neurons, mean, and variance value of whole image, and the cross‐entropy criterion. The function of Ridgelet transform is to identify the straight line marks and fit the regression equation for simulating the course mark in the image. The Ridgelet transform model can be simplified as the combination of Radon and wavelet transforms. The parameters of detected line are acquired by wavelet analysis in Fourier semicircle region. The experiment materials were several plain and interlocked weft‐knitted fabrics with hole, dropped stitch, and course mark defects. The fabric images were captured by an area‐scan camera with a resolution of 600 × 800 pixels, and signal processing was controlled by a digital signal processing multiprocessor on the inspection machine. The validation tests proved that the system performed well.     

基于矿山井下视频监控系统的图像增强方法研究

针对煤矿井下照度低、粉尘多的恶劣环境导致矿山井下图像质量差、图像可分辨率低的问题,提出一种基于脊波变换的图像增强算法,采用多种评价标准对增强后的图像进行评价,并且与直方图均衡化算法、小波增强算法进行比较。结果显示,该算法能够更好地增强图像的边缘、对比度和细节信息,图像效果从整体上更加符合人眼视觉标准。