Edge-directed interpolation based on shifted-linear image model

This paper presents a hybrid image interpolation algorithm to keep details and edges simultaneously. The basic idea is to separate the unknown pixels into two classes and estimate them in different way. One class of unknown pixels is obtained via shifted linear interpolation and the other class through statistical signal processing method. The merit of this hybrid algorithm is that each unknown pixel can be estimated through original pixels simultaneously. Simulation results demonstrate that this hybrid interpolation algorithm improves the quality of the interpolated images over conventional interpolation methods.     

基于结构成分双向扩散的图像插值算法

该文提出一种基于结构成分双向扩散的插值方法,有效地减小了插值图像的边缘扩散,从而获得更为清晰的边缘。该方法采用改进的耦合双向扩散滤波器对轮廓模板插值图像进行边缘增强。其中,为了使滤波器更精确地作用于边缘轮廓,利用形态成分分析(MCA)分离出初始插值图像中的结构分量再实行滤波;同时,改进双向扩散模型,使其能够根据边缘梯度自适应地调整边缘扩散程度,且更加柔和地控制梯度方向的像素值变化。实验结果表明,对比传统的插值方法、相关的边缘自适应插值方法以及几种应用普遍的商用软件,该方法获得的插值图像主、客观质量均有明显提升,不仅有效提高图像锐度,且边缘光滑、过渡自然,避免产生边缘锯齿和过度的人工效应。     

融合模糊增强与改进Canny的植物叶脉边缘提取

针对植物叶脉边缘提取存在的问题,提出一种图 像模糊增强与改进Canny算子相结合的叶脉边缘提取方法。该方法首 先构造分段模糊隶属度函数对高低灰度进行不同处理,然后通过模糊增强函数来增强叶脉两 侧邻接区域灰度对比度,最后对 增强图像采用改进Canny算子提取叶脉边缘。改进Canny算子根据像素点与局部像素的均值差 来自适应的应用高斯滤波,以 减小滤波对边缘的平滑影响;采用Prewitt模板计算梯度幅值和方向,以减小噪声对梯度计 算的影响;采用线性插值技术实现 全向非极大值抑制,以更准确的实现边缘细化。实验结果表明,本文方法在低照度叶片和光 照不均叶片下提取叶脉边缘的 PSNR分别为51.136 dB和50.217 dB,与传统边缘提取方法及 相关文献相比,本文方法对边界模糊性不敏感,可以更加有效地提取完整精细的叶脉边缘。     

AN ADAPTIVE COLOR PLANE INTERPOLATION METHOD BASED ON EDGE DETECTION

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional （1D） gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.     

Reversible data embedding for high quality images using interpolation and reference pixel distribution mechanism

This paper proposes a reversible data hiding method based on image interpolation and the detection of smooth and complex regions in the cover images. A binary image that represents the locations of reference pixels is constructed according the local image activity. In complex regions, more reference pixels are chosen and, thus, fewer pixels are used for embedding, which reduces the image degradation. On the other hand, in smooth regions, less reference pixels are chosen, which increases the embedding capacity without introducing significant distortion. Pixels are interpolated according to the constructed binary image, and the interpolation errors are then used to embed data through histogram shifting. The pixel values in the cover image are modified one grayscale unit at most to ensure that a high quality stego image can be produced. The experimental results show that the proposed method provides better image quality and embedding capacity compared with prior works.     

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.     

非局部特征方向图像插值方法研究

提出了一种非局部的特征方向图像插值方法,有效地保持了插值图像轮廓的光滑,抑制了图像边缘的模糊.这种方法把非局部Hessian矩阵的特征向量视为图像特征方向,使图像能量泛函沿这个方向进行扩散,其扩散强度由图像局部Hessian矩阵特征值参与控制.它克服了传统方法以梯度方向指示图像特征方向的局部性,使图像能量泛函沿正确方向扩散,避免了对图像特征的模糊.数值实验结果显示,该方法既能很好地重建插值图像的边缘,又不会在插值图像中产生伪影或图像边缘失真.     

差分投影快速定向图像插值算法的优化

针对图像放大时分辨率会降低,图像的感知质量变差等问题,并考虑到计算的复杂度,提出了一种差分投影快速定向图像插值算法。该方法通过对低分辨率图像的梯度扩散确定高分辨图像的丢失像素,由特殊的去噪方法对该算法进行优化,并与最近邻插值法与双线性插值算法等相比较。实验结果表明,该方法具有较高的分辨率和较低的计算复杂度,优于其他方法。     

区域指导的彩色图像插值

本文提出了一种针对彩色图像的区域指导的插值算法.该方法首先从低分辨率图像中计算象素沿梯度方向的1、2阶方向导数,然后把彩色图像划分为常数、线性和非线性区域,并对不同的区域施用不同复杂度的滤波器.实验结果表明,与传统的双线性插值相比,该方法可以明显改进插值图像的质量;重建所需的时间也比单纯使用复杂滤波器的时间减少很多,并且可以实现彩色图像的任意放大.     

Spatial error concealment with edge related perceptual considerations

Video transmission over error-prone networks can suffer from packet erasures which can greatly reduce the quality of the received video. Error concealment methods reduce the perceived quality degradation at the receiving end by masking the effects of such errors. They accomplish this by exploiting temporal and spatial correlations that exist in image sequences. Spatial error concealment approaches conceal errors by making use of spatial information only which is necessary in cases where motion information is not available or reliable. The performance of such methods can be greatly increased if perceptual considerations are taken into account as, e.g., the preservation of edge information. This paper proposes a spatial error concealment method that uses edge-related information in order not only to preserve existing edges but also to avoid introducing new strong ones by switching to a smooth approximation of missing information where necessary. A novel switching algorithm which uses the directional entropy of neighbouring edges chooses between two interpolation methods, a directional along detected edges or a bilinear using the nearest neighbouring pixels. Results show that the performance of the proposed method is better compared to both ‘single interpolation’ and to edge strength-based switching methods.     

Subpixel edge detection and estimation with amicroprocessor-controlled line scan camera

A microprocessor-controlled line scan camera system for measuring edges and lengths of steel strips is described, and the problem of subpixel edge detection and estimation in a line image is considered. The edge image is assumed to change gradually in its intensity, and the true edge location may be between pixels. Detection and estimation of edges are based on measurement of gray values of the line images at a limited number of pixels. A two-stage approach is presented. At the first stage, a computationally simple discrete-template-matching method is used to place the estimated edge point to the nearest pixel value. Three second-stage methods designed for subpixel estimation are examined. The modified Chebyshev polynomial and the three-point interpolation method do not require much knowledge on the shape of the edge intensity. If the functional form of the edge is known, a least-square estimation method may be used for better accuracy. In the case of nonstationary Poisson noise, a recursive maximum-likelihood method for the first-stage edge detection, followed by subpixel estimation, is proposed     

A Bayesian approach to image expansion for improved definition

Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion.     

The error-amended sharp edge (EASE) scheme for image zooming.

This paper proposes a new interpolation method, called the error-amended sharp edge (EASE) scheme, which is a modified bilinear method. In order to remove/reduce interpolation artifacts such as image blur and the checkerboard effect (ringing), EASE tries to amend the interpolation error by employing the classical interpolation error theorem in an edge-adaptive fashion. EASE is applied for image zooming by both integer and noninteger magnification factors. The new interpolation scheme has proved to result in high-resolution images having clearer and sharper edges than linear interpolation methods, for all synthetic and natural images we have tested. EASE can be implemented with ease; it turns out to be similarly efficient as cubic interpolation schemes.     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

基于统计特征的彩色图像快速插值方法

本文首先阐述了基于统计特征的图像插值方法,该方法通过提取待插入像素所在区域的协方差矩阵和协方差向量,得出适应于边缘位置和方向的插值权重.为了把基于统计特征的图像插值方法应用于彩色图像插值领域,本文提出了以下措施以提高计算速度:仅对Y图像估计插值权重,并同时应用到R、G、B三个分量的插值;对边缘像素应用基于统计特征的图像插值方法,而对非边缘像素应用简单的双线性插值,即混合图像插值方法.这些措施提高了计算速度,并保证了图像质量.实验表明了该算法在计算速度和插值图像质量方面的有效性.     

Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm

New methods for detecting edges in an image using spatial and scale-space domains are proposed. A priori knowledge about geometrical characteristics of edges is used to assign a probability factor to the chance of any pixel being on an edge. An improved double thresholding technique is introduced for spatial domain filtering. Probabilities that pixels belong to a given edge are assigned based on pixel similarity across gradient amplitudes, gradient phases and edge connectivity. The scale-space approach uses dynamic range compression to allow wavelet correlation over a wider range of scales. A probabilistic formulation is used to combine the results obtained from filtering in each domain to provide a final edge probability image which has the advantages of both spatial and scale-space domain methods. Decomposing this edge probability image with the same wavelet as the original image permits the generation of adaptive filters that can recognize the characteristics of the edges in all wavelet detail and approximation images regardless of scale. These matched filters permit significant reduction in image noise without contributing to edge distortion. The spatially adaptive wavelet noise-filtering algorithm is qualitatively and quantitatively compared to a frequency domain and two wavelet based noise suppression algorithms using both natural and computer generated noisy images.     

Warped distance for space-variant linear image interpolation

The problem of image interpolation using linear techniques is dealt with in this paper. Conventional space-invariant methods are revisited and changed into space-variant ones, by introducing the concept of the warped distance among the pixels of an image. A better perceptual rendition of the image details is obtained in this way; this effect is proved both via the evaluation of the response to an idealized sigmoidal edge model and with experiments on real-world images. The computational costs of the proposed approach are very small when compared to those of state-of-the art nonlinear interpolation operators.     

基于集成成像的深度提取线性插值增强方法

分别用定义的深度提取精密度和深度提取正确度对实验结果进行分析,从结果的整体分布和整体偏差两个方面证明了线性插值对基于集成成像的深度提取具有增强作用。由于单元透镜数量众多,单个图像传感器像素个数有限,传统的基于集成成像的深度提取受到单元图像低分辨率的制约。无需额外的硬件移动,采用线性插值将单元图像像素个数增加一倍,深度提取精密度提升了20%,深度提取正确度提升了15%。集成成像可以同时实现三维场景的重建和深度的提取。这是其他深度提取技术所不具备的特点。通过使用插值后得到的深度信息,使目标物体的重建图像更为清晰。基于集成成像的深度提取可应用于三维场景的背景去除。     

Interresolution Look-up Table for Improved Spatial Magnification of Image

The most commonly used image magnification techniques are interpolation based: nearest neighbor, bilinear, and bicubic. The drawbacks of these traditional methods are that images magnified by the simple nearest neighbor method often appear “blocky,” while images magnified by linear and cubic interpolations usually appear “blurry.” In this work, a new technique, which improves the performance of the traditional image magnification methods, is presented. We show how a differential image pyramid is first constructed using traditional interpolation methods, then how a vector quantizer is designed using the pyramidal data. The vector quantizer is a look-up table, termed the interresolution look-up table (IRLUT), which uses the lower resolution image vector as input to find as its output the corresponding higher resolution image vector. The improved image is produced by using the IRLUT's outputs to compensate for the image magnified by the traditional methods. Experimental results which show that images generated by the current method have sharper edges as well as lower reconstruction mean-square errors than those produced by traditional methods are presented.     

使用方向参数的双三次图像内插方法

传统的双三次内插方法仅在水平和垂直方向估计丢失的像素,易在边缘或纹理区域产生抖动、振铃等现象。为了克服这种现象,提出一种新的使用方向参数的双三次图像内插方法。对待内插的像素,首先在其邻域计算水平、垂直、45°和135°四个方向的梯度,以提取图像局部边缘的强度和方向。对强边缘上的像素,直接沿边缘方向采用对应方向的双三次内插模型估计像素值;否则,先分别沿梯度较大的两个方向采用相应方向的双三次内插模型估计像素,然后采用适当的权系数对所得结果加权平均。此外,为减小计算复杂度,方法中还根据图像局部方差的大小,动态使用双线性内插方法。相比于双三次内插,提出的方法能有效地保存图像边缘和细节;同时,提出的方法还能够实现任意倍数的放大。实验结果表明,与现有的边缘导向的图像内插方法相比,提出的方法具有更好的主观和客观效果,同时计算复杂度并不高。