A new kernel development algorithm for edge detection using singular value ratios

The perceptual quality of an image is very sensitive to the degradation of the edge information which is usually caused by many video signal applications such as super-resolution and denoising. Hence, it is very important to detect and enhance the edge information of the image. In this research work, new sets of kernels for edge detection using ratios of singular values of an image are proposed, which results in more detailed detection of edges in the original image. The parameters, which are the elements of kernel matrices and the threshold value used for producing binary image after convolving the kernels with the image of the proposed method, are optimised to achieve more detailed edge detection of the image. The experimental results show that more detailed edges are detected by the proposed method compared to the conventional edge detection techniques.     

基于自适应阈值HMRF的红外超分辨率重建

在MAP超分辨率重建算法中,相较于Gauss-MRF先验模型,Huber-MRF先验模型具有更好的保持图像边缘和细节的能力,然而对Huber边缘惩罚函数的阈值选取一直没有更好的方式.在考虑红外图像细节纹理信息的基础上,利用图像灰度共生矩阵,把阈值参数与图像细节纹理信息联系起来,实现对边缘惩罚函数阈值的自适应选取,完成超分辨率图像重建.仿真实验证明,该算法获取的高分辨率红外图像具有更高的信噪比,而且更有效地保持了图像的高频信息.     

小波域多角度轮廓模板变分模型的单幅图像超分辨率重建

近年来图像超分辨率重建技术因其可以提高图像的识别精度和识别能力而受到重视,其中一个难点问题是如何保证图像边缘纹理区域的重建质量.本文提出一种基于小波域的单幅图像超分辨率重建方法,首先对输入图像进行非下采样小波变换,根据小波变换的多方向性提出三类多角度模板,并采用TV模型估计各子带轮廓,确定其所属的最优方向,然后利用多角度模板来对各个子带进行双三次B样条插值,最后进行非下采样小波反变换.该方法使重建后图像的边缘、纹理信息更加精细,克服了诸如双线性插值法与双三次插值法等传统插值重建所产生的边缘模糊与边缘锯齿化,以及纹理区域失真等不足,在一定程度上提高了重建图像的质量.该方法可用于图像监控、遥感影像分析和医学图像处理等领域.大量的仿真实验验证了所提出方法的有效性.     

Curvature preserving image super-resolution with gradient-consistency-anisotropic-regularization prior

Single image super-resolution (SR) often suffers from annoying interpolation artifacts such as blur, jagged edges, and ringing. In this paper, we aim to achieve artifact-free SR reconstruction from an input low resolution (LR) image using adaptive de-convolution and curvature refinement. To achieve this, we propose a curvature preserving image SR method based on a gradient-consistency-anisotropic-regularization (GCAR) prior. The gradient consistency term effectively suppresses visual artifacts such as ringing and preserves sharp edges in images while the anisotropic regularization term adaptively preserves the high frequency information according to the gradient magnitude. The complementary two terms are elaborately combined into the GCAR prior for the SR reconstruction. The GCAR prior is very effective in preserving image details and recovering high frequency information. Moreover, we use curvature refinement to remove jagged artifacts caused by aliasing due to decimation. The proposed method employs an effective feedback-control loop which contains adaptive de-convolution, re-convolution, pixel substitution, and curvature refinement. The GCAR prior is utilized in the adaptive de-convolution step. Extensive experiments on various test images demonstrate that the proposed method produces natural-looking and artifact-free SR results in terms of both visual quality and quantitative performance.     

Single image super-resolution using self-examples and texture synthesis

An algorithm for single image super-resolution based on example-based super-resolution and example-based texture synthesis is proposed. While many other techniques for single image super-resolution are mainly effective on edges, the proposed algorithm enhances both edges and texture detail. The algorithm does not use an additional example database as it uses self-examples to synthesize new detail and texture, assuming that images contain a sufficient amount of self-similarity. The texture synthesis component of the algorithm enables the re-synthesis of texture at the output resolution to achieve super-resolution. The algorithm aims to create plausible, visually pleasing detail rather than reconstructing the true high-resolution image. Experimental results for natural images confirm the algorithms ability to create visually pleasing results, but also indicate that its performance is highly content dependent. Future efforts will be aimed at improving the robustness of the method.     

Edge detection using ACO and F ratio

Edge detection is an important step for finding the discontinuities of images and detecting the boundaries of objects. This work presents a novel algorithm for image edge detection using ant colony optimization and Fisher ratio (F ratio)-based techniques. Ants generally search the food from the nest to the food source in the way that maximizes the intensity of pheromone (a chemical secretion). The proposed technique considers that the movements of the artificial ants are steered by the local intensity variation in the image pixel. The directions of ants movements in the image are determined using a direction probability matrix, computed by pheromone and heuristic information of possible directions. In this work, F ratio technique is utilized to determine the optimum threshold value from updated pheromone matrix. This threshold value is further used to extract binary edge map from pheromone matrix. The experiment is conducted on the different test images, i.e., Cameraman, Lena, Coins, Peppers, House and Pillsetc image. The proposed edge detection algorithm is evaluated on the basis of statistical parameters such as kappa, figure of merit, Baddeley’s delta metric and Hausdorff distance, and the experimental results show that the proposed method performs better as compared to earlier reported techniques in most of the cases.     

An Automatic Approach for Accurate Edge Detection of Concrete Crack Utilizing 2D Geometric Features of Crack

The automatic edge detection of cracks on concrete structures plays an important role in the damage assessment process for cracked structures. In this paper, we proposed an automatic method for accurate edge detection of concrete cracks from real 2D images of concrete surfaces containing noisy and unintended objects. In the 2D image of a damaged concrete surface, cracks are usually observed as tree-like topology dark objects of which the branches are line-like and have local symmetry across their center axes. We utilize these two geometric properties of cracks to detect crack edges and discriminate them with edges of other unintended objects. The novel automatic crack edge detection is composed of two sequential stages. In the first stage, cracks are enhanced by a novel phase symmetry-based crack enhancement filter (PSCEF) based on their symmetric and line-like properties while non-crack objects are removed. Estimated crack center-lines are then obtained by thresholding the filtered images and applying morphological thinning algorithm to the binary image. In the second stage, the estimated center lines of the detected cracks are fitted by cubic splines and the pixel intensity profiles in the directions perpendicular to the splines are used to determine the edge points. The edge points are linked together to form the desired continuous crack edges. Various experiments of real concrete crack images are used to demonstrate the excellent performance of the proposed method.     

基于小波的方向自适应图像插值

图像插值是图像处理的一项重要技术,经典的插值算法会产生细节模糊和边缘锯齿现象。该文提出一种基于小波的方向自适应图像插值方法,将小波变换思想和局部方向自适应插值方法结合。为了获得清晰的细节部分,对图像实施改进的方向自适应双线性插值;结合小波方法,提高插值图像的高频细节信息,并进行相关后处理,增强视觉效果。实验结果表明,该文方法插值后的图像边缘清晰光滑,有效抑制了边缘模糊和锯齿现象,相比较传统方法,插值图像的客观质量和视觉效果都得到明显增强,更加适合人眼视觉系统。     

基于图像块分类稀疏表示的超分辨率重构算法

 目前基于图像块稀疏表示的超分辨率重构算法对所有图像块都用同一字典表示,不能反映不同类型图像块间的差别.针对这一缺点,本文提出基于图像块分类稀疏表示的方法.该方法先利用图像局部特征将图像块分为平滑、边缘和不规则结构三种类型,其中边缘块细分为多个方向.然后利用稀疏表示方法对边缘和不规则结构块分别训练各自对应的低分辨率和高分辨率字典.重构时对平滑块利用简单双三次插值方法,边缘和不规则结构块由其对应的高、低分辨率字典通过正交匹配追踪算法重构.实验结果表明,与单字典稀疏表示算法相比,本文算法对图像边缘部分重构质量明显改善,同时重构速度显著提高.     

基于红外图像特征与K-means的边缘检测

为解决红外图像边缘模糊导致边缘提取困难的问题,提出一种基于边缘特征与K-means结合的红外图像边缘检测方法。首先将人眼视觉特性与红外图像边缘点处的灰度分布特点结合,构造出反映其结构特征的数据集;再利用K-means将数据集分为边缘点和非边缘点,提取出图像边缘;最后利用二步法将边缘进行细化,以便实现红外图像边缘检测。实验结果表明:该方法能够通过自适应阈值提取出红外图像的完整外部轮廓,并保留内部边缘信息,对弱边缘起到良好的提取效果,并有效抑制噪声干扰。     

SAR image despeckling via bilateral filtering

Bilateral filtering (BF) can realise both smoothing images and preserving edges, whereas its filtering results are always influenced since its two parameters are difficult to configure to the optimum. In this reported work, the application of BF is extended to synthetic aperture radar (SAR) image despeckling, and the despeckling evaluation indexes, including the equivalent number of looks and the edge save index, are used to estimate the parameters. After BF with estimated parameters imposed on a normalised SAR image, further processing can achieve both despeckling and edge preservation simultaneously. Experimental results show that the visual quality and evaluation indexes of the proposed algorithm outperform the classical Lee filtering.     

基于NSST图像特征的多聚焦图像融合方法

针对广泛存在于多聚焦图像融合方法中的局部图像细节不清晰的现状,提出一种基于非下采样剪切波变换(NSST)图像特征的多聚焦图像融合方法。利用NSST对待融合源图像进行多尺度、多方向稀疏分解,分别获取低频和一系列高频子带图像。通过空间频率和局部能量确定融合后的低频子带系数,利用边缘检测算子直接获取高频子带图像中的细节和边缘信息,并采取NSST反变换得到最终融合结果图像。仿真实验结果表明,同现有的几种经典算法相比,本文提出的方法获得的结果图像拥有更清晰的视觉效果、更理想的客观指标效果以及更高的算法运行效率。     

Edge Preserving Interpolation of Digital Images Using Fuzzy Inference

This paper presents a novel edge preserving interpolation method for digital images. This new method reduces drastically the blurring and jaggy artifacts at the high-contrast edges, which are generally found in the interpolated images using conventional methods. This high performance is achieved by two proposed operations: a fuzzy-inference based edge preserving interpolator and a highly oblique edge compensation scheme developed based on an edge orientation detector. The former synthesizes the interpolated pixels to match the image local characteristics. Hence, edge sharpness can be retained. However, due to the small footage of the fuzzy interpolation method, it cannot avoid edge jaggedness along the highly oblique edges that have very sharp angles against one of the coordinates. Therefore, a segment matching technique is developed to identify precisely the orientation of the highly oblique edges. Combining these two techniques, we improve significantly the visual quality of the interpolated images, particularly at the high-contrast edges. Both the synthesized images (such as letters) and the natural scenes (captured by camera) have been tested and the results are very promising.     

Classified vector quantisation with variable block-size DCT models

The paper describes the classified vector quantisation (CVQ) of an image, based on quadtrees and a classification technique in the discrete cosine transform (DCT) domain. In this scheme, a quadtree is used to segment low-detail regions into variable sized blocks and high-detail regions into uniform 4×4 blocks of various edge and mixed classes. High-detail blocks are classified by an edge-oriented classifier which employs a pattern-matching technique with edge models defined in the normalised DCT domain. The proposed classifier is simple to implement, and efficiently classifies edges to good visual accuracy. The low-detail regions are encoded at very low bit rates with little perceptual degradation, while the encoding of the high-detail regions is performed to achieve a good perceptual quality in the decoded image. Decoded images of high visual quality are obtained for encoding rates between 0.3 and 0.7 bpp     

Adaptive edge image enhancement based on maximum fuzzy entropy

Edges carry the most i mportant information in i ma-ges.Ini mages,edges are marked with discontinuities orsignificant variations in intensity or gray levels,provi-dingthe locations of objects'contours[1].Many edge i m-ages that are processed by some gradi…     

An adaptive nonlinear diffusion algorithm for filtering medicalimages

The nonlinear anisotropic diffusive process has shown the good property of eliminating noise while preserving the accuracy of edges and has been widely used in image processing. However, filtering depends on the threshold of the diffusion process, i.e., the cut-off contrast of edges. The threshold varies from image to image and even from region to region within an image. The problem compounds with intensity distortion and contrast variation. We have developed an adaptive diffusion scheme by applying the central limit theorem to selecting the threshold. Gaussian distribution and Rayleigh distribution are used to estimate the distributions of visual objects in images. Regression under such distributions separates the distribution of the major object from other visual objects in a single-peak histogram. The separation helps to automatically determine the threshold. A fast algorithm is derived for the regression process. The method has been successfully used in filtering various medical images     

改进边缘质量和运动估计的超分辨率图像重构

凸集投影算法（POCS）是一种广泛使用的超分辨率图像重构方法。针对传统的POCS超分辨率图像重构算法出现的边缘模糊及匹配时的局限性问题,首先利用二阶梯度检测出像素周围0°、45°、90°、135°四个边缘。在构造参考帧时采用基于梯度的插值算法,沿边缘方向进行线性插值,沿非边缘方向进行基于一阶梯度的带权插值。在运动估计时,采用SURF匹配算法,提高匹配的鲁棒性和实时性。在修正参考帧时,分别定义中心在四个边缘方向的点扩散函数（PSF）。利用完全参考图像质量评价和无参考图像质量评价分别对仿真实验和实物实验进行了评价,评价结果表明提出的算法较传统POCS算法有明显的改善。      

基于梯度变换的太赫兹图像超分辨率重建

为了提高太赫兹图像的质量，克服边缘模糊的缺陷，采用有理分形插值和基于梯度变换的图像超分辨率重建算法相结合的方法对0.25THz，0.50THz和0.75THz图像进行超分辨率重建实验，并对实验结果进行了定量分析，利用基于空间信息熵的直方图匹配技术和双边滤波器对重建算法进行了优化，增强了该方法的适用性。结果表明，对经过插值的太赫兹图像采用基于梯度变换的超分辨率重建方法处理之后，0.25THz，0.50THz和0.75THz图像的边缘强度分别提高了169%，116%和104%，平均梯度分别提高了16%，28%和24%；同时，成像信号频率和强度也会对重建性能产生影响。该方法可以有效恢复太赫兹图像当中的细节信息，锐化图像边缘，提高图像质量且不会出现振铃现象，具有较好的实用价值。     

An edge detection algorithm based on the multi-direction shear transform

Wavelet multi-resolution analysis allows us to detect edges at different scales. However, the wavelet transform can only capture edge information in three directions, horizontal, vertical and diagonal. In addition, the extracted edges are discontinuous. A new edge detection method to solve these problems is proposed in his paper. Firstly, the image is extended symmetrically by applying horizontal and vertical reflections. Secondly, shear transform is taken on the extended images according to various shear matrixes. Thirdly, the edges of the sheared images are detected by means of wavelet transform. The edges detected in different directions have some difference and can complement each other, so we fuse them with a fusion rule. Finally, a threshold is set to refine the edges. The proposed method works efficiently on the images, and the continuity of the edge is getting better. Besides, the method is able to distinguish the real edges from the noise.     

Hiding digital watermarks using multiresolution wavelet transform

In this paper, an image accreditation technique by embedding digital watermarks in images is proposed. The proposed method for the digital watermarking is based on the wavelet transform. This is unlike most previous work, which used a random number of a sequence of bits as a watermark and where the watermark can only be detected by comparing an experimental threshold value to determine whether a sequence of random signals is the watermark. The proposed approach embeds a watermark with visual recognizable patterns, such as binary, gray, or color image in images by modifying the frequency part of the images. In the proposed approach, an original image is decomposed into wavelet coefficients. Then, multi-energy watermarking scheme based on the qualified significant wavelet tree (QSWT) is used to achieve the robustness of the watermarking. Unlike other watermarking techniques that use a single casting energy, QSWT adopts adaptive casting energy in different resolutions. The performance of the proposed watermarking is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks