Spatially adaptive high-resolution image reconstruction of DCT-based compressed images

The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed observations is considered in this paper. The introduction of compression complicates the recovery problem. We analyze the DCT quantization noise and propose to model it in the spatial domain as a colored Gaussian process. This allows us to estimate the quantization noise at low bit-rates without explicit knowledge of the original image frame, and we propose a method that simultaneously estimates the quantization noise along with the high-resolution data. We also incorporate a nonstationary image prior model to address blocking and ringing artifacts while still preserving edges. To facilitate the simultaneous estimate, we employ a regularization functional to determine the regularization parameter without any prior knowledge of the reconstruction procedure. The smoothing functional to be minimized is then formulated to have a global minimizer in spite of its nonlinearity by enforcing convergence and convexity requirements. Experiments illustrate the benefit of the proposed method when compared to traditional high-resolution image reconstruction methods. Quantitative and qualitative comparisons are provided.     

空域增强算法在医学图像中的应用研究

文中以医学图像为例,对噪声大、模糊不清、对比度低的图像进行增强处理。主要工作是通过几种滤除噪声方法对比,选择中值滤波法去除噪声,然而通过直方图均衡化处理以及对图像的目标区的背景区分别做对数运算和指数运算增强对比度,突显目标区。模拟实验结果表明本文的处理方法具有良好的效果。     

A MODIFIED EDGE-ORIENTED SPATIAL INTERPOLATION ALGORITHM FOR CONSECUTIVE BLOCKS ERROR CONCEALMENT

This paper proposes a low-complexity spatial-domain Error Concealment （EC） algorithm for recovering consecutive blocks error in still images or Intra-coded （I） frames of video sequences. The proposed algorithm works with the following steps. Firstly the Sobel operator is performed on the top and bottom adjacent pixels to detect the most likely edge direction of current block area. After that one-Dimensional （1D） matching is used on the available block boundaries. Displacement between edge direction candidate and most likely edge direction is taken into consideration as an important factor to improve stability of 1D boundary matching. Then the corrupted pixels are recovered by linear weighting interpolation along the estimated edge direction. Finally the interpolated values are merged to get last recovered picture. Simulation results demonstrate that the proposed algorithms obtain good subjective quality and higher Peak Signal-to-Noise Ratio （PSNR） than the methods in literatures for most images.     

Fast on-line B-spline interpolation

A computationally inexpensive algorithm for signal interpolation using B-spline functions is presented. Specifically, the convolution between the B-spline coefficients and the B-spline function itself, widely acknowledged as the most costly aspect of B-spline interpolation, is reformulated such that it is directly amenable to implementation at a much lower computational cost (16.25% of the cost of direct evaluation)     

Shape-based interpolation of multidimensional grey-level images

Shape-based interpolation as applied to binary images causes the interpolation process to be influenced by the shape of the object. It accomplishes this by first applying a distance transform to the data. This results in the creation of a grey-level data set in which the value at each point represents the minimum distance from that point to the surface of the object. (By convention, points inside the object are assigned positive values; points outside are assigned negative values.) This distance transformed data set is then interpolated using linear or higher-order interpolation and is then thresholded at a distance value of zero to produce the interpolated binary data set. Here, the authors describe a new method that extends shape-based interpolation to grey-level input data sets. This generalization consists of first lifting the n-dimensional (n-D) image data to represent it as a surface, or equivalently as a binary image, in an (n+1)-dimensional [(n+1)-D] space. The binary shape-based method is then applied to this image to create an (n+1)-D binary interpolated image. Finally, this image is collapsed (inverse of lifting) to create the n-D interpolated grey-level data set. The authors have conducted several evaluation studies involving patient computed tomography (CT) and magnetic resonance (MR) data as well as mathematical phantoms. They all indicate that the new method produces more accurate results than commonly used grey-level linear interpolation methods, although at the cost of increased computation.     

Fast interpolation algorithm using FFT

An interpolation algorithm for finite-duration real sequences using the discrete Fourier transform is presented. The proposed method is shown to result in a significant saving of computational labour over the discrete version of the time-domain classical interpolation formula. Estimation of inbetween samples for large sequences is possible within a mean square error of 0.00114 with this method. Some considerations with regard to the computation of FFTs are also discussed.     

Shape-based interpolation of tree-like structures in three-dimensional images

Many three-dimensional (3-D) medical images have lower resolution in the z direction than in the x or y directions. Before extracting and displaying objects in such images, an interpolated 3-D gray-scale image is usually generated via a technique such as linear interpolation to fill in the missing slices. Unfortunately, when objects are extracted and displayed from the interpolated image, they often exhibit a blocky and generally unsatisfactory appearance, a problem that is particularly acute for thin treelike structures such as the coronary arteries. Two methods for shape-based interpolation that offer an improvement to linear interpolation are presented. In shape-based interpolation, the object of interest is first segmented (extracted) from the initial 3-D image to produce a low-z-resolution binary-valued image, and the segmented image is interpolated to produce a high-resolution binary-valued 3-D image. The first method incorporates geometrical constraints and takes as input a segmented version of the original 3-D image. The second method builds on the first in that it also uses the original gray-scale image as a second input. Tests with 3-D images of the coronary arterial tree demonstrate the efficacy of the methods.     

图像信号的频域理解

就图像信号的二极傅立叶变换常令人感到困惑的问题进行了较深入的讨论，通过大量的图例，直观地给出了一些相关的概述，并得出了一些重要的结论。     

Fast interpolation algorithm using fast Hartley transform

The use of fast Hartley transform for fast discrete interpolation is considered. The computational method uses the sprit-radix algorithm which requires the least number of operations compared with other Hartley algorithms. Results from this method are compared with those using the fast Fourier transform.     

Hiding information in multispectral spatial images

In this paper, a new method for private geoinformation exchange, entitled access restricted information exchange steganography for spatial information systems (ARS), is presented. ARS hides and recovers a message of substantial length within digital images while maintaining the original image size and dynamic range. Applications for such an information hiding scheme include access restriction to information, authentication, revision tracking of digital signals and covert communication over communication networks. The novel contribution of this paper is to propose a new method of limiting access to the hidden data in image databases (ID) of spatial information systems (SIS).     

Fast eigenspace decomposition of correlated images

We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a two-dimensional (2-D) image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional (3-D) objects rotated about a single axis. In addition, the theory of circulant matrices yields good approximations to the eigendecomposition for images that result when objects are translated and scaled. We use these observations to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that the algorithm performs very well on a number of test cases ranging from images of 3-D objects rotated about a single axis to arbitrary video sequences.     

Fast fractal compression of greyscale images

A new algorithm for fractal compression of greyscale images is presented. It uses some previous results allowing the compression process to be reduced to a nearest neighbors problem, and is essentially based on a geometrical partition of the image block feature space. Experimental comparisons with previously published methods show a significant improvement in speed with no quality loss     

Fast correlative matching of quasi-regular images

Journal of Communications Technology and Electronics - Correlative matching of images spatially different by a shift is considered. For the case when images contain objects with expressed...     

Saturation-based adaptive inverse gradient interpolation for Bayerpattern images

A saturation-based adaptive gradient interpolation algorithm is developed for Bayer pattern images obtained in single-sensor digital cameras. The proposed algorithm uses the concept of pseudo-saturation for 2×2 blocks of the image. The algorithm adapts the scheme of interpolation based on the classification of the pseudo-saturation of Bayer pattern images. Compared with other algorithms, the proposed method preserves fine details, reduces false colours, and distortions around edges and sharp features of the colour image reconstructed from different kinds of Bayer pattern images     

Fast detecting target groups in sar images

A successful algorithm for detecting target groups is presented. Firstly, A global Constant False Alarm Rate （CFAR） detector is utilized to locate the potential target regions, and then the features are computed for target discrimination based on voting mechanism. Finally, Target groups are extracted. The results of experiments show the validity of this algorithm.     

Fast frequency estimation and tracking using Lagrange interpolation

A computationally efficient and accurate frequency estimation and tracking algorithm is proposed, based on the adaptive frequency estimator (AFE) of Etter and Hush. A Lagrange interpolator (a fractional delay filter) is used to estimate the gradient of the performance surface of the adaptation, which enables highly accurate estimation. The performance of the new algorithm is demonstrated in the context of tracking a chirp signal in noise     

Three-dimensional segmentation and interpolation of magnetic resonance brain images

The authors propose a method for the 3-D reconstruction of the brain from anisotropic magnetic resonance imaging (MRI) brain data. The method essentially consists in two original algorithms both for segmentation and for interpolation of the MRI data. The segmentation process is performed in three steps. A gray level thresholding of the white and gray matter tissue is performed on the brain MR raw data. A global white matter segmentation is automatically performed with a global 3-D connectivity algorithm which takes into account the anisotropy of the MRI voxel. The gray matter is segmented with a local 3-D connectivity algorithm. Mathematical morphology tools are used to interpolate slices. The whole process gives an isotropic binary representation of both gray and white matter which are available for 3-D surface rendering. The power and practicality of this method have been tested on four brain datasets. The segmentation algorithm favorably compares to a manual one. The interpolation algorithm was compared to the shaped-based method both quantitatively and qualitatively.     

Subpixel edge localization and the interpolation of still images

In this paper, we present a nonlinear interpolation scheme for still image resolution enhancement. The algorithm is based on a source model emphasizing the visual integrity of detected edges and incorporates a novel edge fitting operator that has been developed for this application. A small neighborhood about each pixel in the low-resolution image is first mapped to a best-fit continuous space step edge. The bilevel approximation serves as a local template on which the higher resolution sampling grid can then be superimposed (where disputed values in regions of local window overlap are averaged to smooth errors). The result is an image of increased resolution with noticeably sharper edges and, in all tried cases, lower mean-squared reconstruction error than that produced by linear techniques.     

Fast, accurate interpolation with B-spline scaling functions

A new method for interpolation using B-spline scaling functions is presented which provides significant advantages compared with previously published approaches using the FFT or DST. Periodisation of the sequence is avoided and constraints on the boundary conditions are relaxed. A simple algorithm is given which yields high accuracy with lower computational complexity than FFT or DST techniques