一种改进的含噪图像边缘检测算法

数字图像边缘检测是图像分割、识别等图像分析和理解领域中的重要基础。针对图像边缘检测中噪声抑制与细节保留之间的矛盾,提出了一种基于小波变换和数学形态学改进的含噪图像边缘检测算法。该算法对含噪图像分别采取小波变换法和数学形态学法进行边缘提取,将所得图像进行小波分解,对高低频系数分别采取不同融合规则进行融合,通过逆小波变换得到融合图像。通过实验对比不同算法对含噪图像的边缘检测效果图,结果表明,该算法提取的图像边缘轮廓信息连续完整,在较大程度上能够抑制噪声,较好地保留了图像的细节信息。     

一种基于小波变换和图像边缘特征的图像融合方法

图像融合是信息融合的重要组成部分,是一种重要的增强图像信息的方法。本文将图像小波变换边缘提取和图像融合结合起来,提出了一种基于图像边缘特征的图像融合方法。首先对输入图像进行小波变换,用模极大值方法检测出图像的多尺度边缘,然后利用边缘特征对小波系数进行融合。实验结果表明该方法既能有效地去除噪声,又能突出源图像的边缘细节信息,是一种有效的图像融合方法。     

改进的基于小波变换的图像边缘检测方法的研究

本文介绍一种基于小波变换模极大值进行图像边缘检测的方法。对图像进行二维小波变换,其梯度模值反映了图像的边缘,用这种方法可以检测到图像所有边缘的细节,但同时也会检测到一些伪边缘和噪声点。本文采用图像分块方法确定阈值,并用该阈值来限定模值,与传统边缘检测方法相比,可以得到更好的边缘检测效果。     

大鱼际掌纹图像边缘提取算法

主要研究大鱼际掌纹图像边缘提取算法。介绍几种经典的边缘检测算子以及Hough变换方法,重点讨论了小波模极大值多尺度边缘检测方法。构造了高斯多尺度边缘检测算子,根据噪声和图像边缘的小波变换模值跨尺度传递的不同特征,研究小波模极大值多尺度边缘检测方法,对大鱼际掌纹图像进行边缘提取。实验结果表明该方法检测到的边缘细节丰富,定位较准确,有效降低了噪声,不足之处是连续性较差。     

多尺度小波在图像测量中的应用

在图像测量中,图像的边缘检测是关键.基于信号和噪声在不同尺度下小波系数模值的变化特征,利用小波变换系数模的局部极大值提取图像的边缘.在对前、后孔配准图像的测量中,能够降低噪声,并能比较精确的得到图像的边缘.     

结合无下采样Shearlet模极大值和改进尺度积的有噪图像边缘检测

为从噪声污染的图像中提取出更为清晰、连续的边缘,进一步改善边缘检测效果,本文提出了一种基于无下采样Shearlet模极大值和改进尺度积的边缘检测方法。首先对含噪图像进行多尺度、多方向无下采样Shearlet变换（Non-subsampled Shearlet Transform, NSST）,得到图像在NSST域的高频系数;然后选取相邻的两个较大尺度的高频系数进行改进的尺度积运算,并经NSST模极大值处理得到边缘二值图像;最后使用区域连通方法去除二值图像中的孤立点,得到准确的边缘图像。大量实验结果表明,与小波模极大值、小波尺度积、基于无下采样Contourlet变换（Non-subsampled Contourlet Transform, NSCT）模极大值和尺度积、NSST模极大值等4种边缘检测方法相比,本文提出的方法具有更强的抗噪能力,且有效地避免了纹理的影响,检测出的边缘完整清晰,连续性好。      

一种基于二进小波变换的图像边缘检测方法

边缘检测是图像处理和计算机视觉领域最活跃的研究课题之一。传统边缘检测方法对噪声非常敏感,针对该问题在传统边缘检测算法分析的基础上,提出了一种基于二进小波变换的图像边缘检测方法。首先,对原图像进行二进小波分解,然后对低频子图像用直方图均衡化来进行增强,对增强后的低频子图像用二进小波变换模极大值点方法进行边缘检测得到边缘图像。实验结果表明,这种边缘检测方法明显优于对原图像直接使用传统边缘检测算子或二进小波变换模极大值点的边缘检测方法。     

一种改进的图像边缘提取算法

针对强干扰背景下的图像在边缘提取时难以在保留边缘细节的同时抑制噪声的情况,提出一种基于二维经验模态分解(Bidimensional Empirical Mode Decomposition,BEMD)和互信息(Mutual Information,MI)的图像边缘提取算法。首先通过BEMD对图像信息进行分解,然后对分解得到的各阶固有模态分量求出能量和能量熵值,并根据互信息准则,通过依次计算相邻分量能量熵之间的互信息值来区分高频和低频信号。最后,结合小波变换模极大值和数学形态学两种方法的优点分别对高低频信号进行边缘检测,叠加融合得到图像边缘。结果表明,此方法提取出的图像边缘连续完整,并保持了边缘的细节特征。     

基于小波变换和数学形态学的遥感图像边缘检测

由于遥感图像一般带有较大的噪声，通过实验表明，用传统的边缘检测方法效果不理想。采用小波方向性检测技术并结合数学形态学的方法，提出了一种基于小波变换和小尺度的数学形态学的遥感图像边缘检测方法。实验表明本算法计算有效，边缘定位准确，对噪声有一定的抑制作用，边缘检测效果明显。     

基于提升小波和形态学融合的图像边缘检测

针对传统边缘检测方法边缘定位不精确的缺点,结合提升小波变换和数学形态学的优势,提出了一种融合提升小波和多尺度形态学的边缘检测方法.首先,对原始图像进行提升小波变换;再用小波变换和多尺度形态学算子分别对低频图像进行边缘检测,根据异或原则融合成低频边缘;然后,用小波变换检测高频图像边缘;最后,通过提升小波反变换得到边缘图像.实验结果表明,与传统或其他的形态学边缘检测方法相比,该算法在保持图像边缘清晰的同时,具有很强的边缘定位能力.     

基于多层级图像分解的图像融合算法

不同类型的探测器在成像机理上有不同的侧重点,使得成像图像表征的信息也有所不同,导致单幅图像不能完整地反映场景的有效信息。因此,提取多源图像的互补信息,并去除其中的冗余信息,合成一幅能准确、完整表达场景的复合图像的技术成为了图像处理领域中一项非常重要的技术,图像融合正是这类问题的一种有效解决方法。针对传统多尺度分解的图像融合方法易产生噪声和信息缺失的现象,文中提出了一种基于多层级图像分解的红外与可见光图像融合算法。首先,利用加权平均曲率滤波的边缘保持特性与高斯滤波的平滑特性,构建了多层级图像分解模型。在利用该模型将源图像分解为小尺度层、大尺度层和基层等3个不同层级。然后,针对基层,采用能量属性融合策略进行融合;针对大尺度层,采用复合融合策略进行融合;针对小尺度层,采用最大值融合策略。最后,将融合后的层级进行加和,以重构出最终的融合图像。实验结果表明:文中提出的基于多层级图像分解的图像融合算法能够有效降低噪声产生的概率,同时减少了融合后的信息缺失。     

Spatial-domain image hiding using image differencing

A method to embed a secret image into a cover image is proposed. The method is based on the similarity among the grey values of consecutive image pixels as well as the human visual system's variation insensitivity from smooth to contrastive. A stego-image is produced by replacing the grey values of a differencing result obtained from the cover image with those of a differencing result obtained from the secret image. The process preserves the secret image with no loss and produces the stego-image with low degradation. Moreover, a pseudorandom mechanism is used to achieve cryptography. It is found from experiment that the peak values of signal-to-noise ratios of the method are high and that the resulting stego-images are imperceptible. Even when the size of the secret image is about a half of the cover image     

基于图像增强和复原的图像去雾方法研究

雾霭等天气下获得的图像存在对比度低、颜色退化、景物模糊等一系列图像退化的问题,直接影响了对图像信息的有效利用。因此,对雾天图像进行有效的去雾处理,有效改善降质图像的质量,具有一定的实际意义。分析讨论基于图像增强的多尺度Retinex算法和利用图像复原原理的基于暗原色先验理论的去雾算法,并对具有不同特点的单幅有雾图像进行去雾仿真。实验结果表明,不同理论基础的两种去雾算法各有特点,基于暗原色理论处理得到的图像去雾效果更显著,算法运行速度更快。     

Prototype image constraints for set-theoretic image restoration

Constraints based on prototype images are developed and used in set-theoretic image restoration. A prototype can be obtained as a result of applying a predetermined operator to the observed image. In this case, the operator and the bound, which limits the variation of the restored image from the prototype, are the two defining quantities of a prototype constraint. General guidelines for rigorously estimating the defining bound of a prototype constraint under certain simplifying conditions are discussed. The authors provide two examples of prototype constraints where the prototypes are obtained by the Wiener filtering operator and a local averaging operator. The projection onto convex sets algorithm using the prototype constraints is applied to both monochrome and color images degraded by out-of-focus blur at different noise levels. The results show significant improvement over the Wiener restoration in reducing the restoration artifacts     

Dual-branch deep image prior for image denoising

In this work, we propose a two-stage denoising approach, which includes generation and fusion stages. Specifically, in the generation stage, we first split the expanding path of the UNet backbone of the standard DIP (deep image prior) network into two branches, converting it into a Y-shaped network (YNet). Then we adopt the initial denoised images obtained with DAGL (dynamic attentive graph learning) and Restormer methods together with the given noisy image as the target images. Finally, we utilize the standard DIP on-line training routine to generate two complementary basic images, whose image quality is quite improved, with the help of a novel automatic iteration termination mechanism. In the fusion stage, we first split the contracting path of the standard UNet network into two branches for receiving the two basic images generated in the previous stage, and obtain a fused image as the final denoised image in a fully unsupervised manner. Extensive experimental results confirm that our method has a significant improvement over the standard DIP or other unsupervised methods, and outperforms recently proposed supervised denoising models. The noticeable performance improvement is attributed to the proposed hybrid strategy, i.e., we first adopt the supervised denoising methods to process the common content of images substantially, then utilize the unsupervised method to fine-tune the specific details. In other words, we take full advantage of the high performance of the supervised methods and the flexibility of the unsupervised methods.     

Underwater image enhancement with image colorfulness measure

Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristic-level training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.     

Blind image deconvolution for single motion-blurred image

Motion blur due to camera shake during exposure is one of the most common reasons of image degradation,which usually reduces the quality of photographs seriously.Based on the statistical properties of the natural image's gradient and the blur kernel,a blind deconvolution algorithm is proposed to restore the motion-blurred image caused by camera shake,adopting the variational Bayesian estimation theory.In addition,the ring effect is one problem that is not avoided in the process of image deconvolution,and usually makes the visual effect of the restored image badly.So a dering method is put forward based on the sub-region detection and fuzzy filter.Tested on the real blurred photographs,the experimental results show that the proposed algorithm of blind image deconvolution can remove the camera-shake motion blur from the degraded image effectively,and can eliminate the ring effect better,while preserve the edges and details of the image well.     

利用反演可见光图像的方法实现红外图像的获取

为了取得较逼真的红外图像,提出了一种利用可见光图像反演红外图像的新方法.首先,将可见光图像分割为"物体"和"大气"两个区域,由各区域与环境辐射的关系计算出在特定波段内进入仿真红外热像仪的总辐射出射度.然后,模拟红外热像仪的内部工作参数,根据红外热像仪的工作原理计算出各区域自身的辐射出射度,从而得到各区域的温度值.最后,由红外图像温度值与灰度值的映射关系,将各区域的温度值以灰度值的形式表现出来,即得到了仿真的红外图像.实验结果表明,此方法可有效地提高反演效果,具有较强的实用性.