一种基于数据融合的图像插值算法

针对传统的线性插值算法存在的边缘模糊问题,本文提出一种新算法.首先采用距离平方反比的插值方法在插值点邻域内计算水平、垂直和对角三个方向共6个插值,然后以插值距离和方向梯度构造权重,进行数据融合获得最终插值.该算法既考虑了插值距离因素,又考虑了插值方向梯度信息,有效地保护插值图像的边缘和纹理信息.实验结果表明,该算法的插值图像比传统的双线性插值法均方误差降低而平均梯度增加,是一种提高插值图像分辨率的有效方法.     

图像插值的改进自适应核回归方法

针对 steering 自适应核回归的插值算法中图像整体灰度值偏离较大和空洞的现象,本文提出了一种基于改进的自适应核回归函数的图像插值算法.该方法根据图像的自相似性原理,通过把图像邻域中像素间几何距离函数融入 steering 自适应核函数中,实现一种改进的自适应核函数,并采用这种核函数对图像进行回归插值.实验结果表明...     

一种新的超分辨率图像重建算法

超分辨率重建是提高图像分辨率的一种方法.根据图像分层变换和插值处理的特点,提出了一种将分层子带变换S+P变换(序列变换加预测运算)和插值算法相结合的超分辨率重建算法.该方法首先对图像序列进行运动估计和运动补偿,然后再对配准后的图像序列进行S+P变换,并对分解得到的高频子带进行插值,最后通过S+P逆变换得到超分辨率图像....     

一种反梯度自适应插值算法

针对边缘方向插值算法计算量大、边界的处理效果不理想,而自适应放大算法对权值因子的选择太过简单的问题,提出了一种新算法。根据数字图像边缘区域的灰度值变化强烈,而非边缘区域的灰度值变化比较平滑的特点,在边缘处沿边缘方向作线性插值,非边缘区域则采用反梯度自适应插值的方法。试验结果表明,此算法不仅能降低运算复杂度,还能获得清晰的图像边缘,进一步提高了插值图像的视觉质量。     

一种改进的声呐图像自适应增强算法

针对声呐图像增强算法研究较少等问题，提出了一种具有较强自适应能力改进图像增强算法，该算法通过自适应选择方差最小的滑动窗口作为当前像素点的增强处理窗口，并自适应判别像素点所属的区域类别，以进行相应的增强处理。仿真与现有算法的结果对比表明，本方法具有算法简单、易于实现等特点，且增强效果可较好地满足视觉效果的需求。     

小波双三次插值搜索算法提高遥感图像分辨力

通过小波双三次插值中高频外推阈值门限选取与峰值信噪比变化关系的分析,提出了小波双三次插值搜索算法。该算法能够自动搜索到高频外推的最佳阈值门限,在不破坏光学遥感图像原始信息的情况下,提高图像的空间分辨力和峰值信噪比,有利于对图像的细节信息进行观察分析。实验表明,该算法的重建图像的峰值信噪比比全小波插值图像和小波双线性插值图像的峰值信噪比分别高6.5dB和2.4dB,熵提高到原图像的1.3倍,是一种提高光学遥感图像分辨力的有效算法。     

红外图像中的自适应维纳滤波噪声抑制技术

红外图像存在信噪比低、对比度差的问题,在目标辐射强度低、距离远以及恶劣天气状况下尤为严重.为了提高图像信噪比,提出一种红外图像中的自适应维纳滤波噪声抑制算法.算法突破了维纳滤波要求未退化图像和噪声功率谱已知的技术瓶颈,基于Canny边缘检测算子构建了平滑区域噪声方差的邻域估计准则,实现了红外图像噪声的自适应抑制.实验表...     

自适应插值算法在图像修复中的应用研究

针对BSCB模型速度慢、整体变分模型易产生阶梯效应的缺点,提出了一种基于扩散率函数的自适应插值算法。该算法利用待修复像素点周围的已知像素点的梯度权函数和距离权函数,对未知像素点进行赋值。实验结果显示: 将该算法应用到灰度图像和彩色图像的修复中,与传统算法相比所需运算时间短,视觉效果较好,定量评价指标(峰值信噪比)也证明了该算法的有效性。     

一种改进多分辨率图像融合算法

提出一种基于局部熵的多分辨图像融合算法。利用小波变换得到待融合图像的多分辨结构,同时得到图像的多分辨局部熵序列。以局部熵为判据,在图像多分辨结构相应各级上进行融合,得到融合图像的多分辨结构,利用小波逆变换重构融合图像。实验结果表明,该图像融合方法在保留TM多光谱图像光谱分辨率的同时,通过融合SPOT全色图像提高了空间分辨率,丰富了图像细节信息。     

小波自适应比例改进算法在图像去噪中的应用

为了改善图像质量和便于识别、压缩等处理,图像去噪是必不可少的过程。提出一种改进的小波比例萎缩去噪算法,摒弃单一根据功率谱构造萎缩函数的方法,在函数式中加入自适应阈值策略。利用局部信号与噪声的小波系数方差比确定阈值,具有局部适应性强,重建误差小的特点。Matlab仿真实验在原始图像加入N（0,20^2）白噪声,去噪后图像均方误差减小226,峰值信噪比提高了3．722dB;而且重建图像视觉效果更好,并能保持图像边缘细节。     

An improved adaptive detail enhancement algorithm for infrared images based on guided image filter

Detail enhancement algorithms are important for raw infrared images to improve their overall contrast and highlight important information in them. To solve the problems that current algorithms like GF&DDE have, an improved adaptive detail enhancement algorithm for infrared images based on a guided image filter is proposed in this paper. It chooses the threshold for the base layer image adaptively according to the histogram statistical information and adjusts the mapping range of the histograms according to the dynamic range of the image. Besides, the detail layer is handled by a simpler adaptive gain control method to achieve the good detail enhancement effect. Finally, the base layer and the detail are merged according to the approximate proportion of the background and the details. Experimental results show that the proposed algorithm can adaptively and efficiently enhance different dynamic range images in different scenarios. Moreover, this algorithm has high real-time performance.     

3D image interpolation based on anisotropic diffusion of feature point correspondence

An automatic method has been developed to interpolate between neighboring slices in a gray‐scale data set by anisotropic diffusion of feature point correspondence. The feature point extracted is registered to form the feature vector. Thus a three dimensional (3D) weight anisotropic vector diffusion is introduced to spread the feature vector to the correspondence vector, which estimates spatial correspondence between adjacent slices. Bilinear interpolation is made along the direction of correspondence vector. Experiments are performed on medical data sets to evaluate the proposed method, showing that the new algorithm achieves good quality and improvement in efficiency relative to the traditional methods. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 338–345, 2013     

Morphologic field morphing: Contour model-guided image interpolation

An interpolation method using contours of organs as the control parameters is proposed to recover the intensity information in the physical gaps of serial cross-sectional images. In our method, contour models are used to generate the control lines required for the warping algorithm. Contour information derived from this contour model-based segmentation process is processed and used as the control parameters to warp the corresponding regions in both input images into compatible shapes. In this way, the reliability of establishing the correspondence among different segments of the same organs is improved and the intensity information for the interpolated intermediate slices can be derived more faithfully. To improve the efficiency for calculating the image warp in the field morphing process, a hierarchic decomposition process is proposed to localize the influence of each control line segment. In comparison with the existing intensity interpolation algorithms that only search for corresponding points in a small physical neighborhood, this method provides more meaningful correspondence relationships by warping regions in images into similar shapes before resampling to account for significant shape differences. Several sets of experimental result are presented to show that this method generates more realistic and less blurred interpolated images, especially when the shape difference of corresponding contours is significant. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 480–490, 1997     

An adaptive method based on the improved LPA-ICI algorithm for MRI enhancement

Various diseases are diagnosed using medical imaging used for analysing internal anatomical structures. However, medical images are susceptible to noise introduced in both acquisition and transmission processes. We propose an adaptive data-driven image denoising algorithm based on an improvement of the intersection of confidence intervals (ICI), called relative ICI (RICI) algorithm. The 2D mask of the adaptive size and shape is calculated for each image pixel independently, and utilized in the design of the 2D local polynomial approximation (LPA) filters. Denoising performances, in terms of the PSNR, are compared to the original ICI-based method, as well as to the fixed sized filtering. The proposed adaptive RICI-based denoising outperformed the original ICI-based method by up to 1.32 dB, and the fixed size filtering by up to 6.48 dB. Furthermore, since the denoising of each image pixel is done locally and independently, the method is easy to parallelize.     

一种改进的基于对数谱估计的语音增强算法

提出了一种基于对数谱估计的改进型语音增强算法。相对于传统语音增强算法,在语音信号存在不确定的条件下,利用软判决增益因子修正技术调正带噪语音信号的对数谱幅度,抑制背景噪声。引入的改进型先验信噪比估计和语音信号先验不存在概率估计方法,能够有效地估计得出语音信号的存在概率,进而求得语音信号存在时的谱增益因子函数,联合语音信号不存在时设定的增益因子函数加权求得谱增益函数。计算机仿真表明,即使在低信噪比条件下,输入背景噪声为高斯白噪声和粉红噪声等加性白噪声时,所提算法对噪声的抑制效果非常明显,且有效地克服了传统算法中引入的“音乐噪声”和语音信号畸变。     

一种改进的EMD图像分解算法

根据图像系统所固有的自相似性以及经验模式分解(EMD)算法的完备性和稳定性,给出了一种完整、快速、高效的EMD图像分解算法.该算法主要改进了EMD图像分解的曲面插值方法以及提取固有模式图像的结束条件,解决了一般EMD图像分解算法存在的图像分解算法速度慢、三角剖分导致漏点现象、图像分解算法的结束条件不明确的问题.最后,利用Madab进行的仿真实验验证了该算法的有效性.     

一种改进的推广自适应相干累积算法

论文主要研究了LMS算法、ACI算法、GACI算法的原理和性能；并对GACI算法加以改进，以降低其参数对背景噪声变化的敏感性；将算法用来提取高斯白噪声背景下的线性调频脉冲信号，并用软件仿真器进行仿真。仿真结果表明了改进的GACI算法的优越性——参数对背景噪声功率变化的敏感性明显降低，并证明了其可靠性、可行性以及工程可实现性。     

一种改进的图像序列运动小目标检测方法

针对目标背景运动的情况,在随机哈夫变换运动检测方法(MDRHT)的基础上,提出了基于中心偏移的哈夫变换运动检测方法(Center-Biased MDHT)。它利用图像中显著的边界信息,有效地估计图像序列帧间图像背景的整体运动;利用图像背景运动的一致性,预先设定帧间位移的阈值,减少了预处理阶段的计算量;对前后两帧相邻图像进行差分处理,充分利用小目标在差分图像中成对出现的特点,达到了在目标检测阶段减少计算量的目的。     

一种改进的全景图生成算法

介绍了全景图的生成过程，对目前常见的几种图像拼接算法进行了分析和比较，在此基础上，提出了一种改进的全景图像生成算法．该算法通过对初始图像进行预处理、改进特征点选取方法以及优化特征点匹配过程，提高了全景图像生成的效率和准确性．实验结果也表明，该算法可广泛应用于基于图像的虚拟场景构造等方面．