一种从低分辨率图像序列获取高分辨率图像的算法

在某些应用领域,常常需要得到目标的高分辨率图像,考虑到在这些应用场合中,往往可以获得对同一景物或目标的多帧图像,本文提出了一种基于亚像素级图像配准与类似于中值滤波插值的从多幅低分辨率(LR)图像中获取一幅高分辨率(HR)图像的算法。算法首先采用梯度方法计算出LR图像之间的位移量,经过图像配准后,每个HR像素点被赋予其作用域内所有LR像点值的中值。仿真结果表明,该算法简单有效,既能提高图像分辨率,又能较好地去除非线性噪声。     

Fractal image compression based on Delaunay triangulation andvector quantization

Presents a new scheme for fractal image compression based on adaptive Delaunay triangulation. Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey level dependent way. The triangulation is thus fully flexible and returns a limited number of blocks allowing good compression ratios. Moreover, a second original approach is the integration of a classification step based on a modified version of the Lloyd algorithm (vector quantization) in order to reduce the encoding complexity. The vector quantization algorithm is implemented on pixel histograms directly generated from the triangulation. The aim is to reduce the number of comparisons between the two sets of blocks involved in fractal image compression by keeping only the best representative triangles in the domain blocks set. Quality coding results are achieved at rates between 0.25-0.5 b/pixel depending on the nature of the original image and on the number of triangles retained.     

Delaunay三角形剖分约束下的图像配准算法

针对一般的特征点匹配只考虑了特征点本身蕴含的信息,容易发生误匹配的问题,给出一种基于Delaunay三角形剖分(Delaunay Triangulation, DT)的图像匹配算法。该算法对参考图和感知图中可能的正确匹配点进行DT剖分,对剖分后的图形进行对比并找出一致的结构,进而得到正确匹配点。实验结果表明该算法可以有效地抑制SURF（Speed Up Robust Feature,SURF）中的错误匹配,增强匹配可靠性,与RANSAC（Random Sample Consensus, RANSAC）法相比,可以大幅缩短计算时间,在基于点的图像配准方面具有较高的应用价值。      

Compression of image block means for non-equal block partition schemes using Delaunay triangulation and prediction

An approach based on applying Delaunay triangulation to the compression of mean values of image blocks that have non-identical shape and size is proposed. It can be useful for image compression methods that require the use of image partition schemes with non-equal block sizes, such as fractal and DCT-based image coding. Some aspects of practical realisation of the proposed method are discussed. Evaluation of the performance of the proposed method is carried out and comparisons with some conventional methods are made, demonstrating the potential of the method.     

Integrated recurrent neural network for image resolution enhancement from multiple image frames

The paper presents a new method for image resolution enhancement from multiple image frames using an integrated recurrent neural network (IRNN). The IRNN is a set of feedforward neural networks working collectively with the ability of having feedback of information from its output to its input. As such, it is capable of both learning and searching the optimal solution in the solution space for optimisation problems. In other words, it combines the advantages of both the Hopfield network and the multilayered feedforward network in solving the enhanced image reconstruction problem. Simulation results demonstrate that the proposed IRNN can successfully be used to enhance image resolution. The proposed neural network based method is promising for real-time applications, especially when the inherent parallelism of computation of the neural network is explored. Further, it can adapt itself to the various conditions of the reconstruction problem by learning     

基于Delaunay三角形网格的彩色视频帧间编码方法

充分利用彩色视频亮度和色度分量之间的相关性,提出了一种基于DT网格的彩色视频帧间编码方案。该方案在运动估计时仅对亮度分量Y进行DT(Delaunay triangulation)描述,对亮度分量的网格节点进行连续运动估计,利用6参数方法得到三角形内像素点的运动矢量,经过相似变换得到色度分量的运动矢量。另外对残差图像进行特殊处理和编码。实验结果表明,相对于适合低码率传输的H.263编码方法,在相同的压缩比下该方案解码图像有更好的主客观质量。     

CCD图像传感器的高分辨率技术

评价了 Ｃ Ｃ Ｄ 图像传感器与电子束摄像管的性能。分析了高分辨率 Ｃ Ｃ Ｄ 像感器的技术难点。阐述了解决这些问题的技术措施：双 Ｈ－ Ｃ Ｃ Ｄ 移位寄存器、 Ｓ Ｒ Ｄ 法、 Ｃ Ｃ Ｓ 法及微透镜技术等。给出了三种新器件的技术指标,并与常规方法构成的器件进行了比较     

Enhancement of low spatial resolution image based on highresolution-bands

Thermal infrared measurements of Band 6 acquired by Landsat TM sensor have lower spatial resolution than those of the other six bands. The authors propose a statistical approach to enhance the resolution of low spatial resolution image by using remaining bands. They employ a multivariate normal distribution for the seven band values. The values of Band 6 are predicted by the conditional expectations. Validity of their procedure is examined by mean squared errors based on actual images     

基于艇载遥感成像数据的高空间分辨率全色遥感图像仿真

高空间分辨率全色遥感图像在军事侦察、地面监视等领域具有较高的应用价值.为模拟星载全色遥感图像, 提出了一种由艇载遥感成像系统获取的低空遥感图像为数据源的高空间分辨率全色遥感图像仿真方法.首先将低空宽视场图像按典型地物类型进行监督分类, 其次将低空宽视场图像与多光谱图像按不同地物类型分类拟合, 并将多光谱拟合结果合成高空间分辨率全色仿真图像, 最后对高空间分辨率全色仿真图像进行仿真精度评价.相比星载全色遥感图像, 仿真图像同样具备高空间分辨率、全色波段、宽视场等特点.仿真方法可为星载全色遥感图像仿真提供较准确的数据支撑.     

Wavelet domain image resolution enhancement using cycle-spinning

A wavelet domain image resolution enhancement method is proposed. The method adopts the cycle-spinning methodology adapted for use in the wavelet domain. The perceptual and objective quality of resolution enhanced images compare favourably with recently emerged algorithms in the field     

大气湍流模糊图像的高分辨力复原算法

大气湍流是大气中的一种重要运动形式,它的存在使大气中的动量、热量、水气和污染物的垂直和水平交换作用明显增强,这种干扰作用极大地影响了光电成像系统对于目标的分辨能力。由于湍流影响而退化的图像中同时存在着幸运区域,用适当的算法可以获得高分辨力复原图像。为了获取包含幸运区域的大气湍流模糊图像,在实验室使用人造湍流,并结合短曝光技术拍摄了大气湍流干扰的序列图像。文中应用矩形交叠分块方法,改进了基于偏微分方程（PDEs）的序列图像复原算法,对获取的序列短曝光图像进行处理。结果表明,经该算法处理得到的合成图像质量有明显的提升,该算法对大气湍流造成的图像质量退化有较好的复原作用。     

应用DBS技术在SAR/GMTI模式下高分辨率成像

多普勒波束锐化成像技术(DBS)能为检测出的动目标定位提供参考，从而有效地实现精确打击。DBS技术在弹道导航、地形匹配等许多方面有着广泛的应用。首先简述了DBS成像方法的一般工作原理，经分析得出结论：DBS成像在SAR，GMTI模式下应用时，如果引入聚焦型的SAR处理方法．即通过去距离单元走动、去方位向调频斜率等方法可有效提高雷达成像质量。最后利用雷达实测数据进行处理．得出了较满意的高分辨DBS成像结果。     

Spectral linear mixing model in low spatial resolution image data

Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral linear mixing model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The linear mixing model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies.     

High resolution and low distortion digital look-up-table sinusoidal oscillators

We propose two new methods to reduce the spurious harmonic distortion of a digital sinusoidal signal generated using the look-up-table (LUT) method. Spurious harmonic distortion arises when we try to interpolate the value of a sample that is not present in the LUT. The first proposed method depends on a real time evaluation of the missing sample value without the need for any additional LUT length. The second proposed method requires an additional table and some real time processing to calculate the value of the interpolated sample. The two proposed methods are simulated and their performance is compared with that of the existing methods; namely direct LUT, linear interpolation and trigonometric interpolation. The simulation results show that our proposed methods are superior to both direct LUT and linear interpolation methods. Moreover, for practical table lengths, the performance of our proposed methods is of the same order as that of the trigonometric interpolation method.     

A wavelet technique applied to estimate image shifts and enhance image resolution during processing of a sequence of shifting digital frames

The problem of high-resolution image reconstruction from noisy frames that are received by a photodetector array and shifted with respect to each other is considered. A wavelet technique is proposed for solution of this problem. The technique uses a 2D wavelet decomposition as a parametric model of the brightness function of an image in the plane of the photodetector array. A recurrent-iteration algorithm is developed for combined estimation of variable frame shifts and of the coefficients of the wavelet docomposition corresponding to the doubly improved resolution. It is shown that, under various conditions, the algorithm provides for estimation of shifts with an error no greater than 0.06 of the array pitch and for estimation of an observed-object’s brightness function with a resolution twice as high as the input-frame resolution.     

Feasibility of enhanced microwave image resolution using a superdirective aperture

Microwave imaging systems with small numerical apertures have low resolution. It is shown that substantial enhancement of resolution is possible if the recorded data can be loaded with a superdirective function. The criterion for use of this technique is that the recorded data are sufficiently accurate.     

Super resolution image reconstruction through bregman iteration using morphologic regularization

Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.     

High resolution NMR spectroscopy using a recursive algorithm

When the DFT (discrete Fourier transform) is used as a method for obtaining NMR (nuclear magnetic resonance) spectrum, resolution of the obtained NMR spectrum depends clearly on spin-spin relaxation times of the nucleus and the sampled number of NMR signal. That is, the NMR spectrum is affected by decay of NMR signal due to the relaxation times and errors associated with discrete approximation of NMR spectrum. As a results, its resolution is degraded and the spectrum disagrees with the density distribution of the nucleus. In the present paper, we propose a new NMR spectrum estimation method for reducing their effects, demonstrating a high resolution NMR spectrum which is closer to the density distribution than that of the DFT. The proposed method is derived from applying the Kalman filter or the recursive least-square method to a NMR signal model based on Bloch equation. Under various conditions, the proposed method is compared with the DFT using a proton 1H NMR signal observed from mayonnaise in a 2 [T] static magnetic field. The results show that if spin-spin relaxation times are known previously, the proposed method can provide a higher resolution NMR spectrum than the DFT. In addition, it is shown that the method can reduce the discrete approximation errors of NMR spectrum. Finally, we demonstrate that if frequency components of sources of NMR signal are present in a limited range in frequency domain of interest, the method can magnify NMR spectrum with a sampling time and interval fixed.     

High resolution transport spectroscopy in ultimate MOSFETs at very low temperature

To analyse electrostatic disorder in doped sub-100 nm Si-MOSFET channels we investigate the sub-threshold regime by transport spectroscopy at very low temperature. We measure resonances in the differential drain current and analyse their distribution in gate voltage, which is found to be gaussian. The mean value is interpreted as the electrostatic charging energy associated to a local well in the surface potential, whose dimensions is comparable to the channel length. The channel dopants concentration does not affect significantly this result.