符合人眼视觉感知特性的改进PSNR评价方法

针对重构立体图像的视觉感知效果较好,而峰值信噪比(PSNR)值却很低的问题,提出一种基于加权滤波、隶属度函数以及滑动窗的方法来改进PSNR值,使其符合人眼视觉感知特性。实验结果表明,经本文方法改进的PSNR值与人眼视觉感知特性一致,从而提高了PSNR评价方法在立体重构图像中的通用性。

一种基于PSNR的迭加量化公开水印算法

图像有好的视觉质量又要有高的鲁棒性是当今数字水印研究的热点与难点之一。提出了一种在确定图像质量的前提下具有高鲁棒性的公开水印算法,在其嵌入算法中,首先确定嵌入水印图像的质量——通过控制图像的峰值信噪比(PSNR)来实现,再根据图像所要达到的峰值信噪比来设计水印嵌入步长;另外,水印信号是通过权平均的方式迭加量化到图像DCT域的多个低中频系数上,根据人眼视觉系统(human visual system)的特性,按图像内容自适应量化嵌入,很好地解决了不可见性和鲁棒性之间的矛盾。实验结果表明,该水印算法能保证图像具有很高的视觉质量,同时对于常见的图像处理和攻击具有很好的鲁棒性。

DCT域基于PSNR的扩频水印算法

数字水印技术是信息隐藏和版权维护的一种有效方法。在先前的研究基础上提出了DCT域中如何根据水印图像与原始图像间的PSNR来估计水印的多重水印嵌入强度,并由此提出一个基于PSNR的扩频水印强度的估计公式。实验结果表明,提出的嵌入强度估计公式误差小,具有一定的准确性,算法具有一定的鲁棒性。

基于不变矩和PSNR的相似图像检测工具

以图像特征提取为重点,基于图像特征提取,相似性度量,以及图像质量评估算法,实现了一个相似图像检索的工具。本系统使用MATLAB编程语言,从图像预处理着手,进而用不变矩方法中的Hu矩的方法对图片形状特征进行提取,接着用相似性度量算法中的曼哈顿距离法对特征进行相似性匹配并进行图像分类,再用PSNR图像质量评估算法计算出每类图像中最好的一张并将其显示出来,最后再利用MATLAB的GUI实现一个简洁明了的用户界面,提供了方便人机交互的接口。系统界面清晰,操作简单,对相似图像的检测具有较好的效果。

基于PSNR空域量化索引调制水印参数定量估算

量化索引调制方法是常用的水印嵌入和信息隐藏方法,量化步长是该方法中决定嵌入深度的参量,目前往往是通过反复试验方法确定该参量的取值,因此影响水印嵌入速度。针对此问题,以抖动量化调制方法为例,根据量化误差的分布情况给出了基于量化步长的量化误差定量估算;以该定量估算为基础,以空域像素为量化系数推导出了量化步长、水印数据量与PSNR之间的定量关系式。实验结果显示,在相同情况下通过实验测得的PSNR值和定量关系式计算的PSNR值基本一致,验证了推导的定量关系式的正确性。

融合注意力机制的GAN病虫害图像超分辨率重建

收集咖啡和柑橘病虫害样本图片,利用TensorFlow深度学习框架,在原始SRGAN(Super-resolution generative adversarial networks)的超分辨率重建网络里加入了注意力模块,对重建图像视觉质量和峰值信噪比(PSNR)、结构化相似性(SSIM)指标进行分析。结果表明,设计的模型和原始SRGAN模型对比之后峰值信噪比提高了2.23,结构相似性提高了7%。在细节纹理方面可以获得更好的视觉效果,重建后的图像识别准确率提高了约4.42个百分点。因此,设计的模型可以对小样本性质的植物病虫害样本进行扩充。

High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden data.This property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image generation.However,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical equations.In addition,these numbers may cluster in certain ranges.The hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can detect.Therefore,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego image.This paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data concealment.The AVL tree algorithm provides several advantages for image steganography.Firstly,it ensures balanced tree structures,which leads to efficient data retrieval and insertion operations.Secondly,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image quality.The data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel extraction.The green channel serves as the foundation for building a balanced binary tree.First,the sender identifies the colored cover image and secret data.The sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated information.The next step is to create a balanced binary tree based on the green channel.Utilizing the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue channel.The first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret data.After embedding the bits in the binary tree level by level,the model restores the AVL tree to create the stego image.Ultimately,the receiver receives this stego image through the public channel,enabling secret data recovery without stego or crypto keys.This method ensures that the stego image appears unsuspicious to potential attackers.Without an extraction algorithm,a third party cannot extract the original secret information from an intercepted stego image.Experimental results showed high levels of imperceptibility and security.

Elevating Image Steganography:A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential priority.To overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure communications.Most of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB substitution.In this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)substitution.The proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three bits.The proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment capacity.The achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 dB.These findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

无线网络中基于PSNR的视频数据流传输仿真分析

针对无线网络环境低带宽、高错误率和高时延的特点,在NS2中建立仿真模型,利用基于PSNR的客观质量评估方法,分别对不同的数据包错误率、压缩量化参数、GOP的长度、数据包的长度对于图像质量的影响进行仿真分析.仿真结果给出的结论为无线网络更加有效地进行视频数据流传输提供了有益的参考.

基于PSNR小波域量化调制水印嵌入方法的参数定量估算

量化步长是量化调制方法中的重要参量,但是目前无法定量计算。针对这个问题,以抖动量化调制方法为研究对象,以小波变换系数为量化系数,提出了基于峰值信噪比(PSNR)的量化步长定量估算方法。首先根据量化误差的分布情况给出了基于量化步长的量化误差定量估算方法,然后根据小波变换的性质推导出了量化步长、水印序列长度与PSNR之间的定量关系式。实验结果显示,量化步长取值相同时实验测得的PSNR值和定量关系式计算出的PSNR值基本一致,验证了推导的定量关系式。

Effect of Peak Power and Pulse Width on Coherent Doppler Wind Lidar’s SNR

The laser device is the core component of coherent Doppler wind lidar.The peak power and pulse width of laser transmitting pulse have important effects on SNR.Based on coherent Doppler wind pulse lidar,the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis,and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar.But when the peak power is too large,the laser pulse may appear nonlinear phenomenon,which cause the damage of the laser.So,the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy,but it will also make the spatial resolution lower,and the influence of turbulence on SNR will be greater.After a series of simulation analyses,it can be concluded that when the peak power is 650W and the pulse width is 340ns,the SNR of the system can be maximized.In addition,the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification.The experimental results are consistent with the theoretical analysis and simulation,which verifies the correctness of the theoretical analysis and simulation results.It provides theoretical basis and practical ex-perience for the design of laser transmitting pulse in coherent Doppler wind lidar system.

单帧红外图像超分辨率重建算法研究

针对目前基于插值的红外图像超分辨率重建算法主要把重心置于原始图像的像素上的问题,提出了一种将原始图像像素和插值像素相结合的红外图像超分辨率重建算法。该算法在最近邻插值算法的基础上,将其与自适应高斯滤波相结合,采用边插值边滤波的方式对红外图像进行超分辨率重建处理。由红外数据集的大量对比试验结果可知,该算法能够在不以时间消耗为代价的前提下使重建结果得到有效提升。具体表现为其重建结果的客观评价指标与经典的图像插值算法相比提高了约2.8%~3.2%,并且能够有效减少噪声、锯齿和模糊现象。该算法有助于推动红外图像超分辨率重建在军事、医疗等领域的发展。

基于改进小波阈值函数的仪表图像去噪方法研究

传统小波去噪的软阈值函数和硬阈值函数在去噪过程中产生的恒定偏差与抖动会导致去噪后的图像存在模糊和伪吉布斯现象。基于此问题,该文提出了一种软硬兼顾的加权平均阈值函数,此阈值函数避免了传统阈值存在的“一刀切”问题,降低恒定偏差的同时具有连续性与渐进性。其次在改进传统阈值门限上,设计了一种随着分解层数增加而减少的自适应门限阈值,避免消除深层数的高频边缘信息。最后通过对含有不同高斯白噪声的仪表图片进行去噪,计算峰值信噪比与均方误差并对结果图像进行的客观分析评估,实验结果表明,该阈值函数方法相比于其他方法,去噪结果更为清晰,均方误差最小,峰值信噪比最大,去噪效果更佳。

基于整体变分模型的随机值脉冲噪声去除方法

为了有效地检测随机值脉冲噪声并去除干扰,提出一种新的开关整体变分去噪方法。根据不同密度的随机值脉冲噪声,分别在3 px×3 px、5 px×5 px和7 px×7 px的窗口邻域内进行噪声检测及检测结果修正,对判断为噪声的像素点采用基于整体变分模型的方法去噪,再采用动态阈值对去噪后的图像进行多次噪声检测和噪声去除,从而进一步提高去噪效果。仿真实验结果表明:对于不同密度的随机值脉冲噪声图像,该方法在有效去除噪声的同时还可以较好地保护图像的细节信息,峰值信噪比相比其他方法提高显著。

基于深度学习的图像风格迁移方法研究

为了快速实现图像风格迁移效果,提出基于深度学习的图像风格迁移方法。先将预处理目标内容图像和风格图像输入预训练VGG-19网络进行特征提取,再对特征损失系数进行设置,进而引入权重因子和平衡损失系数的最小二乘惩罚函数以加权求和方式获取总损失系数,最后依据梯度下降迭代方法进行图像重建,实现图像风格迁移效果。实验采用峰值信噪比(PSNR)和均方误差(MSE)来评价风格迁移图像的性能。结果显示,整体取得了很好的图像风格迁移效果。

Paired regions for shadow removal approach based on multi-features

The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.

Underwater Image Enhancement Using MIRNet

In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.

基于压缩感知归一化关联成像实现目标重构

在归一化关联成像的基础上,结合压缩感知理论,提出了基于压缩感知的归一化关联成像方法.该方法首先对物臂的桶探测值进行归一化处理,并由散斑场构造测量矩阵;然后采用正交匹配追踪算法,在低测量次数下优质量地还原出了物体的像.实验中采用灰度图像及二值图像作为成像目标,以峰值信噪比作为衡量标准,分别对传统关联成像,归一化关联成像及压缩感知归一化关联成像的重构效果进行了量化对比.仿真实验结果表明,对于细节较为丰富的灰度图像,压缩感知归一化关联成像的峰值信噪比较传统方法高6 dB左右,比归一化关联成像方法提高了2 dB左右;对于细节较少的二值图像,其峰值信噪比较归一化关联成像法高3.4～4.3 dB,比传统法高5.2～6.5 dB.最后,采用实际电荷耦合元件测得的散斑场构造了测量矩阵,实验结果进一步验证了基于压缩感知的归一化关联成像算法能提高重构质量.

基于新阈值函数的小波阈值去噪算法

针对传统小波阈值函数在阈值处的不连续性、小波估计系数存在偏差等不足,导致去噪后的图像出现失真、产生吉布斯震荡等问题,提出了一种改进的阈值函数,与常用的硬阈值、软阈值以及已有改进的阈值函数相比,该函数不仅易于计算,而且具有优越的数学特性。为了验证该阈值函数的优越性,通过仿真实验对几种小波去噪方法的峰值信噪比(PSNR)与均方差(MSE)进行了对比。实验结果表明,此去噪方法无论是在视觉效果上,还是在均方差和信噪比性能分析上均优于常用的阈值函数。

高密度椒盐噪声的多方向加权均值滤波

提出一种适用于去除高密度椒盐噪声的图像滤波算法,以进一步提高输出图像的峰值信噪比。利用直方图形状判定椒盐噪声的两种灰度值,用于噪声像素的检测与定位。对于非噪声像素,直接输出灰度值;对于噪声像素,沿其邻域的k个方向分别搜索一个距离最近的非噪声像素,然后以欧式距离倒数为权重,采用k个非噪声像素的加权灰度均值作为噪声像素的输出灰度值。测试了不同的方向数k对滤波性能的影响,确定了k的最佳取值为4。采用该方法对椒盐噪声密度为10%到90%的图像进行滤波,输出图像的峰值信噪比比现有同类方法提高了1.8~4.7 d B。该方法有效提高了高密度椒盐噪声图像的滤波质量,处理速度满足实时要求。