Developing an algorithm for compression, multiplexing and enhancement of multiple images

Image compression is one of the important fields that has useful applications in data storage and transmission. In this research a new algorithm is developed and tested for multiple-image compression and enhancement. The algorithm, in addition, is applied to multiple noisy images. Also, the effect of compression ratio on the peak signal to noise ratio (PSNR) is explored by applying different compression ratios. The developed algorithm gives good compression and noise immunity. It can be used for storage/transmission of encrypted and compressed information.     

Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain

Image denoising is always the basic problem of image processing, and the main challenge is how to effectively remove the noise and preserve the detailed information. This paper presents a new image denoising algorithm based on the combination of trivariate prior model in nonsubsampled dual-tree complex contourlet transformlet transform (NSDTCT) domain and non-local means filter (NLMF) in spatial domain. Firstly, NSDTCT is constructed by combining the dual-tree complex wavelet transform (DTCWT) and nonsubsampled directional filter banks (NSDFB). The noisy image is decomposed by using NSDTCT. Secondly, based on the correlation between the interscale and intrascale dependencies of NSDTCT coefficients, the distribution of the high frequency coefficients is modeled with the trivariate non-Gaussian distribution model. A nonlinear trivariate shrinkage function is derived in the framework of Bayesian theory, and then the denoised coefficients are obtained and inverse NSDTCT is performed to get the initial denoised image. Finally, NLMF is used to smooth the initial denoised image. Simulation experiment shows that our algorithm can obtain better performances than those outstanding denoising algorithms in terms of peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM) as well as visual quality.     

基于小波域中值滤波和运动估计的去隔行算法

本文提出了一种新的自适应去隔行算法,该方法首先将小波分解引入到去隔行算法的预处理阶段,然后利用运动估计以及混合中值滤波的特点,充分考虑相邻像素间的方向空间相关性,有效保持图像中的边界部分,并减小了运动补偿插值后的误差,尤其是对纹理信息丰富的地方得到了很好的插值效果.实验结果表明,无论是从客观上的信噪比还是主观测评来分析,图像的效果比传统的算法有了提高,能更好的满足人类对画面质量的要求.     

Real-time restoration of rotational blurred image using gradient-loading

The key to the restoration of rotational motion blurred image is how to restore the image under a low cost and to correct the irreversibility of the degradation function matrix.Based on the special qualities of degradation function matrix and precise deduction in space-domain, we present a new approach using gradient-loading for restoration of rotational blurred image.By easily adding a gradient operator, the irreversibility of the original matrix is corrected and can be applied for inverse filtering then.Gradientloading is the optimized approach which combines the advantages of both the approaches using constrained least square filtering and traditional diagonal-loading.Compared with the approach using least square filtering, its peak signal-to-noise ratio (PSNR) is improved from 3.18 to 6.46dB, while the computing time is reduced to 1/2-1/3.Experimental results demonstrate the effectiveness, noise-resistibility, robustness, and low complexity of this approach, which make it more suitable for real-time environment.     

基于混沌粒子群优化的图像Contourlet阈值去噪

提出了基于混沌粒子群优化的图像Contourlet阈值去噪方法.该方法在Contourlet变换域内利用混沌粒子群算法来确定最优阈值,再通过软阈值函数去噪,且不需要噪音方差等先验信息.实验结果表明：该方法与小波Bayeshrink阈值、基于粒子群的小波阈值、Contourlet自适应阈值等去噪方法相比,能有效地去除高斯白噪音和椒盐噪音的混合噪音,提高峰值信噪比,并较好地保留图像的细节和纹理,从而明显地改善了图像的视觉效果.     

高精度全景补偿电子稳像

针对摄像机拍摄目标过程中自身的随机抖动造成的视频序列不稳定,以及稳像补偿过程中边缘信息的丢失,提出了基于SURF（Speed-up Robust Feature）算法的全景电子稳像方法。首先,运用SURF算法提取当前帧图像和参考帧图像的兴趣点,将两幅图像的兴趣点进行匹配,建立两帧的对应关系。针对兴趣点数目较少及场景中部分区域特征相似的情况,引入了兴趣点位移一致性抑制策略,改进了RANSAC（RANdom SAmple Consensus）误匹配的剔除算法,使得运动矢量的精确度小于1 pixel。然后,判定参考帧的更新策略,获取平滑的运动变量。最后,进行运动补偿,运用图像镶嵌技术对丢失的边缘区域信息进行全景补偿,得到了高精度的全景稳像结果,实验得到的输出视频峰值信噪比（PSNR）提高了33.1%。     

Estimation and visualization of longitudinal muscle motion using ultrasonography: A feasibility study

Ultrasonography is a convenient and widely used technique to look into the longitudinal muscle motion as it is radiation-free and real-time. The motion of localized parts of the muscle, disclosed by ultrasonography, spatially reflects contraction activities of the corresponding muscles. However, little attention was paid to the estimation of longitudinal muscle motion, especially towards estimation of dense deformation field at different depths under the skin. Yet fewer studies on the visualization of such muscle motion or further clinical applications were reported in the literature. A primal–dual algorithm was used to estimate the motion of gastrocnemius muscle (GM) in longitudinal direction in this study. To provide insights into the rules of longitudinal muscle motion, we proposed a novel framework including motion estimation, visualization and quantitative analysis to interpret synchronous activities of collaborating muscles with spatial details. The proposed methods were evaluated on ultrasound image sequences, captured at a rate of 25 frames per second from eight healthy subjects. In order to estimate and visualize the GM motion in longitudinal direction, each subject was asked to perform isometric plantar flexion twice. Preliminary results show that the proposed visualization methods provide both spatial and temporal details and they are helpful to study muscle contractions. One of the proposed quantitative measures was also tested on a patient with unilateral limb dysfunction caused by cerebral infarction. The measure revealed distinct patterns between the normal and the dysfunctional lower limb. The proposed framework and its associated quantitative measures could potentially be used to complement electromyography (EMG) and torque signals in functional assessment of skeletal muscles.     

使用计算全息进行三维信息加密的方法研究

全息加密技术作为一种特殊的加密方法被广泛应用于信息加密和防伪等领域。在全息加密过程中,光波的波长、记录距离和入射角度等参数用做加密密钥和解密密钥被人们深入研究,但所加密的信息几乎都是二维信息。利用一种基于虚拟光学的对三维信息进行加密的方法,将三维物体的大小作为一个新的密钥被引入安全全息加密算法,其安全性能得到了极大提高;在此基础上提出了对再现三维图像进行客观评价的方法,即修正峰值信噪比法(PSNR,PeakSignal-to-Noise Ratio)。模拟实验证明,计算全息方法(CGH,Computer-Generated Hologram)作为一种对三维信息进行加密的方法是可行的,修正PSNR法对再现三维图像的质量进行定量分析是有效的。     

Denoising Convolutional Neural Network with Energy-Based Attention for Image Enhancement

In the realm of image denoising, the use of convolutional neural networks (CNNs) has lately gained traction. Several activities involve the utilization of excellent-clarity pictures and recordings. Images were captured in a wide variety of illumination circumstances, which means that not all of them are of the highest quality. Low-light photography suffers from a decline in perceived image quality because of the restricted dynamic range of the pixel values. Therefore, it is vital to enhance the appearance of images. Maximum texture retention is achieved by the structural similarity index-loss-based method. The suggested discrete wavelet transform (DWT)-self attention (SA)-Denoising convolutional neural networks (DnCNNs) make use of state-of-the-art techniques for image denoising like energy band analysis, very deep architecture, learning algorithms, dense-sparse-dense training, and regularization approaches. DnCNN is intended to remove the hidden layers" latent, yielding a pure picture. After a degraded input sample has had its relevant energy features retrieved using DWT, the perfect image enhancement is achieved thanks to the incorporation of the self-attention mechanism. Second, a hierarchical-branch network is formed by combining the suggested network with the denoising CNN and additional loss in order to reduce the reliance on the amount of noisy data in multi-modal picture analysis and make the problem of image enhancement more tractable. In the end, DWT-SA-DnCNN"s self-learning qualities are used to improve image quality by obtaining features including undesirable noisy data, edge factor, texture, uniform and non-uniform areas, smoothness, and object structure. Simulation results show that our hybrid DWT-SA-DnCNN-based contrast enhancement strategy outperforms state-of-the-art methods.     

Optimum adaptive array stochastic resonance in noisy grayscale image restoration

Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.