基于二进小波变换的边缘保持图像插值算法

利用离散二进小波变换(DDWT)所具有的良好的多尺度边缘提取特性以及子带间的相关性,提出了基于DDWT的边缘保持图像插值算法。算法利用多尺度边缘的指数衰减规律预测损失掉的高频子带中的边缘,并利用三次样条插值算法恢复损失掉的高频子带中的非边缘信息。最后通过离散二进小波合成得到高分辨率图像。实验结果显示,该算法优于传统的双线性和双三次插值算法。对于像Lena这样的纹理较少的图像其峰值信噪比(PSNR)提高了2dB以上,而对其它图像插值的结果也有不同程度的质量改善,插值的结果更符合人的视觉系统特性。     

Morphological Image Enlargements

This paper presents a method based in mathematical morphology to enlarge images. It does not make the low pass assumption which is common to all linear interpolation methods and which does not often hold for images. Pixels in smooth areas are properly interpolated by linear methods while those at the edges are not. The method begins with a linear interpolation and a gradient computation. The gradient serves as a measure of confidence about the linear interpolation. Then, the proposed algorithm processes the pixels in a certain order: first pixels with high confidence (smooth zones) of the image and those with a low one (edges) at the end. By doing so, it preserves both slow variations and sharp edges. The method can be applied to other image processing problems, such as edge enhancement or motion vector estimation, where there is an image and confidence information about each pixel.     

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.     

自适应边缘保持图像缩放及VLSI设计

提出的自适应边缘保持图像缩放方法,以自适应的方式对传统的线性插值方法非线性修正,使得不同位置的像素采用相应的插值方法.边缘保持插值算法能消除图像边缘模糊,锯齿效应,棋盘效应等,达到增强插值图像的效果.本算法处理后的放大图像的边缘更加锐利,能够实现图像的无级放大,可应用于实际的数字视频处理芯片中.     

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。     

Markov random field model-based edge-directed image interpolation.

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.     

Feature preserving image interpolation by adaptive bidirectional flow

Most image interpolation algorithms currently used suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This letter presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines （edges）, while a normal diffusion is done to remove artifacts （＂jaggies＂） along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first order and the second order directional derivatives of the image. Experimental results on the Lena image demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.     

Demosaicing using optimal recovery.

Color images in single-chip digital cameras are obtained by interpolating mosaiced color samples. These samples are encoded in a single-chip charge-coupled device by sampling the light after it passes through a color filter array (CFA) that contains different color filters (i.e., red, green, and blue) placed in some pattern. The resulting sparsely sampled images of the three-color planes are interpolated to obtain the complete color image. Interpolation usually introduces color artifacts due to the phase-shifted, aliased signals introduced by the sparse sampling of the CFAs. This paper introduces a nonlinear interpolation scheme based on edge information that produces high-quality visual results. The new method is especially good at reconstructing the image around edges, a place where the visual human system is most sensitive.     

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.     

一种改进的线性图像插值算法

针对传统的双线性插值法在对图像进行插值后会不可避免的产生边缘模糊的问题,提出了一种改进的线性插值法,该算法首先把待插值点分为三类,然后分别选取合适的已知点进行插值。通过对经典图像lena和pepper进行插值的实验结果表明,该算法的插值效果与双立方法相当,但计算量远远小于双立方,能有效的保持图像边缘信息,提高了图像质量。     

Edge Detection of Highly Distorted Images Suffering from Impulsive Noise

A new edge detection method for highly distorted images, which suffer from impulsive noise, is introduced. The proposed method comprises three main stages; analysis for the impulsive behavior of the image pixels, restoration of the pixels which have impulsive behavior and finally, estimation of the edges. The simulation results reveal that the proposed method shows better performance than the other methods mentioned in this paper in the cases of preserving the details and detecting the edges correctly and continuously, especially when the noise ratio is very high.     

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。     

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.     

基于角点保护的偏微分方程图像插值方法

图像插值是数字图像处理领域中的一个基本问题,利用插值技术可以实现图像的缩放和图像恢复。传统的插值方法易使边缘产生锯齿效应或使插值结果模糊不清。该文提出一种基于角点保护的偏微分方程插值方法。该方法针对图像中不同的特征进行特定插值,在保持边缘结构清晰的同时,还能保护尖锐的角形结构,有效改善插值后图像的整体视觉效果和峰值信噪比。此外,对方程的主要参数进行分析,提出了相应的参数确定方法,从而提高该方法的适用性。     

AN ADAPTIVE SPECKLE SUPPRESSION AND EDGE ENHANCEMENT TECHNIQUE

Speckle degrades severely the quality of medical B-scan ultrasonic images, especially it blurs edges and details of images. An adaptive speckle suppression and edge enhancement method based on Nakagami distribution is presented. The statistics of log-compressed echo images is derived for Nakagami distribution, An adaptive filter based on local statistical property of speckle is designed. The stick technique that utilizes sticks with different sizes and various orientations is applied to locally approximate certain linear features of image. The local regionis a stick insted of a usual window, the orientation of sticks is decided by hypothesis test optimizing method and the length of sticks is obtained by region growing technique. Performance of the new method has been tested on the phantom and ultrasound images of pig muscle and echocardiographic. The results show that the technique effectively reduces the speckle noise while preserving and enhancing the tissue edge and resolvable details.     

New edge-directed interpolation

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.     

A new orientation-adaptive interpolation method.

We propose an isophote-oriented, orientation-adaptive interpolation method. The proposed method employs an interpolation kernel that adapts to the local orientation of isophotes, and the pixel values are obtained through an oriented, bilinear interpolation. We show that, by doing so, the curvature of the interpolated isophotes is reduced, and, thus, zigzagging artifacts are largely suppressed. Analysis and experiments show that images interpolated using the proposed method are visually pleasing and almost artifact free.     

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.     

基于梯度最小准则的线性插值修正算法

针对传统的线性插值存在的图像边缘模糊问题,提出了一种改进方法。根据边缘与梯度之间的关系,以插值点梯度最小为准则,对线性插值结果进行修正,实现保护插值图像的边缘信息。实验结果表明,该方法与传统的线性插值法相比,插值图像的平均梯度提高,均方误差减小,是一种有效提高插值效果的方法。