Edge adaptive image resolution enhancement in video sensor network

Video sensor network usually uses fairly low-resolution images due to the limited transmission bandwidth in transmitting images. It is potential to enhance the captured low-resolution images using image resolution enhancement technique that is able to produce a high-resolution image from its low-resolution counterpart. The key challenge of image resolution enhancement is to preserve the edge structure in images. In this paper, a new image resolution enhancement approach is proposed to estimate the intensity of the unknown pixel using a bilateral weighted average of that of its neighboring pixels. More specifically, the neighboring pixels with nearer distance have larger contributions. Furthermore, the neighboring pixels belonging to direction with smaller variation have larger contributions. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms. Furthermore, the proposed approach yields low computational complexity; it is potential for real-time implementation.     

Photoacoustic microscopy image resolution enhancement via directional total variation regularization

Photoacoustic microscopy(PAM) is recognized as a powerful tool for various microcirculation system studies. To improve the spatial resolution for the PAM images, the requirements of the system will always be increased correspondingly. Without additional cost of the system, we address the problem of improving the resolution of PAM images by integrating a deconvolution model with a directional total variation regularization. Additionally, we present a primal-dual-based algorithm to solve the associated optimization problem efficiently. Results from both test images and some PAM images studies validate the effectiveness of the proposed method in enhancing the spatial resolution. We expect the proposed technique to be an alternative resolution enhancement tool for some important biomedical applications.     

Fingerprint image enhancement using data driven Directional Filter Bank

A new method based upon data driven tool, principal component analysis (PCA), for fingerprint enhancement is proposed in this paper. PCA is a very useful statistical technique that has found application in many different fields like image compression, face recognition and is commonly used for finding patterns in data of high dimension. In the proposed method, the input image is first decomposed into directional images using decimation free Directional Filter Bank (DDFB). Then these directional images are normalized. A data driven technique PCA is applied to these normalized directional fingerprint images, which gives the PCA filtered images. These are basically directional images. Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.     

一种适用于胸部CR图像自适应增强方法

人体胸部各种组织器官较多,造成胸部的CR(Computed Radiography)图像动态范围大,是所有临床CR图像中最为复杂的一种。一般的图像增强方法只对胸腔中的部分组织起到了增强的效果,而对另一部分组织不会有良好的增强的效果,甚至会出现对比度变差、细节模糊、噪声增加等不理想的结果。提出了基于邻域灰度方差与噪声方差之比的自适应增强方法,可根据胸部CR图像的灰度特性调节增强程度的加权因数K,自适应的增强胸部CR图像边缘细节,丰富图像的细节信息,提高图像的诊断价值。实验证明,该算法处理后的胸部CR图像细节丰富,信噪比高,细节方差和背景方差之比(DV/BV)比通用算法高,增强后的CR图像具有良好的视觉效果,是一种有效的适合胸部CR医学图像的边缘细节增强方法。     

Infrared image detail enhancement based on local adaptive gamma correction

An infrared image detail enhancement method based on local adaptive gamma correction (LAGC) is proposed. The local adaptive gamma values are designed based on the Weber curve to enhance effectively the image details. Subsequently, the active grayscale range of the image processed by LAGC is further extended by using our proposed histogram statistical stretching. The experimental results show that the proposed algorithm could considerably increase the image details and improve the contrast of the entire image. Thus, it has significant potential for practical applications.     

Blind image deconvolution with spatially adaptive total variation regularization

A blind deconvolution algorithm with spatially adaptive total variation regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish edges from flat areas. The proposed algorithm can effectively reduce the noise in flat regions as well as preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Comparative results on simulated and real degraded images are reported.     

High resolution mosaic image of capillaries in human retina by adaptive optics

Adaptive optics (AO) has been proved as a powerful means for high resolution imaging of human retina. Because of the pixel number of charge-coupled device (CCD) camera, the field of view is limited to 1°. In order to have image of capillaries around vivo human fovea, we use mosaic method to obtain high resolution image in area of 6°×6°. Detailed structures of capillaries around fovea with resolution of 2.3 μm are clearly shown. Comparison shows that this method has a much higher resolution than current clinic retina imaging methods.     

Global brightness and local contrast adaptive enhancement for low illumination color image

An adaptive enhancement algorithm is proposed in the paper for low illumination color image which is low brightness and low contrast. The algorithm is realized through three steps: the global brightness adaptive adjustment, locally adaptive contrast enhancement and color restore. Experiments show that the brightness and local contrast of low illumination color image can be effectively enhanced. The details, especially in the dark region of enhanced image are more prominent and the enhanced image is more vivid than some other methods for low illumination color image enhancement.     

High resolution mosaic image of capillaries in human retina by adaptive optics

Adaptive optics (AO) has been proved as a powerful means for high resolution imaging of human retina.Because of the pixel number of charge-coupled device (CCD) camera, the field of view is limited to 1°.In order to have image of capillaries around vivo human fovea, we use mosaic method to obtain high resolution image in area of 6°× 6°. Detailed structures of capillaries around fovea with resolution of 2.3μm are clearly shown. Comparison shows that this method has a much higher resolution than current clinic retina imaging methods.     

The effect of Laplacian filter in adaptive unsharp masking for infrared image enhancement

Image processing, in particular image enhancement techniques have been the focal point of considerable research activity in the last decade. With the aid of an existing image enhancement technique, adaptive unsharp masking (AUM), we propose a novel kernel to be used in AUM filtering in order to enhance discontinuities which occur on the edges of targets of interest in infrared (IR) images. The proposed method uses an adaptive filter approach where an objective function is minimized by using descent algorithms. The output IR image has better sharpness and contrast adjustment for the detection of targets in terms of objective quality metrics. Hence, the proposed method ensures that the edges of the targets in IR images are sharper and that the quality of contrast adjustment has its optimum level in terms of peak signal-to-noise ratios.     

基于像素灰阶熵的自适应增强算法在乳腺CR图像中的应用

乳腺完全是由密度接近软组织构成,因此乳腺CR(Computed Radiography)放射图像对比度小,轻微的差别变化都可能是肿瘤的表现,要对其进行增强处理方能满足医生临床诊断的需要。而目前通用的乳腺CR图像增强算法对比度和噪声增强过度,丢失细节,为此提出了采用一种基于像素灰阶熵的自适应边缘增强算法对乳腺CR图像进行增强。该算法使用模糊影像增加对选择空间频率的响应,以增强乳腺CR图像结构边缘和细节;算法能根据乳腺CR图像灰度特性即像素灰阶熵来自适应的调节增强程度的加权因数K。实验证明,该算法处理后的乳腺CR图像细节丰富,信噪比高,增强后的图像具有良好的视觉效果,该算法是一种有效的适合乳腺CR医学图像的边缘细节增强算法。     

Directional edge enhancement using a liquid-crystal display

We present a method for orientation-selective enhancement of the first derivatives of an image. The proposed method is based on the polarization properties of the twisted-nematic liquid-crystal displays (LCDs). LCDs are manufactured to work between a polarizer-analyzer pair, and for a given incident polarization direction, the image contrast will depend on the analyzer orientation. In particular, two analyzer orientations of interest could be identified for the proposed experiments: one that reproduces the image displayed on the LCD, and the other one (orthogonal to the former) that gives a negative (contrast reversed) replica of the original image. By placing a beamsplitter after the LCD, two images are spatially separated, analyzed by linear polarizers in perpendicular directions, and then slightly displaced. When the original image and its displaced (contrast reverted) replica are imagined together across a plane, one obtains a resultant image with enhanced first derivatives along a specific direction. Unlike the usual Fourier (coherent) image processing, the technique proposed here works with incoherent illumination. Validation experiments are presented.     

Directional couplers in trapped image guide configuration

Dispersion characteristics and wave impedance of trapped coupled image guides are computed by using mode matching techniques. Dispersion curves for trapped coupled image guide are plotted for various dielectric materials and dimensional parameters. The hybrid directional couplers in the above configuration have been theoretically designed by computing the normalization propagation constant.     

图像增强算法综述

图像增强算法能够提高图像整体和局部的对比度,突出图像的细节信息,使增强后的图像更符合人眼的视觉特性且易于机器识别,在军事和民用领域具有广泛的应用。本文从图像增强算法的原理出发,归纳总结了近年来应用比较广泛的4类图像增强算法及其改进算法,包括直方图均衡图像增强算法、小波变换图像增强算法、偏微分方程图像增强算法和基于Retinex理论的图像增强算法。结合人眼视觉特性、噪声抑制、亮度保持和信息熵最大化等图像增强的改进算法,在保证增强图像具有较高对比度的前提下,可进一步提升图像的质量。实现了9种较为典型的图像增强算法,采用主观和客观的评价方法对增强效果进行了对比,分析了不同增强算法的优缺点,并给出了这些算法的计算时间。对这些算法的深入研究能够推动图像增强技术向更高水平发展,从而使图像增强技术在多个学科领域发挥重要作用。     

Saliency adaptive super-resolution image reconstruction

A saliency-based approach is proposed in this paper to perform super-resolution image reconstruction, with the aim to produce a higher-resolution image based on its low-resolution counterparts. The proposed approach adaptively adjusts the degree of regularization using the saliency measure of the local content of the image. Furthermore, a gradient-based criterion is proposed to measure the saliency of the image. Experiments are conducted to demonstrate the superior performance of the proposed approach.     

多分辨分形理论在高分辨力遥感图像分割中的应用

针对遥感图像的高分辨力特性,提出了基于多分辨分形的遥感图像分割方法。改进了多分辨结构的建立方法,提出了基于Laplacian金字塔的多分辨结构的分形分割法。实验表明,改进的多分辨分形图像分割方法在减少信息冗余度的同时,提高了数据利用率,可有效地对高分辨力遥感图像进行分割分类。     

Spectral resolution enhancement by chemical shift imaging

Three-dimensional chemical shift imaging (3D CSI) with appropriate data postprocessing can be used as a tool to improve spectral resolution in samples where large susceptibility differences and limited shim capabilities prevent good sample shimming. Data postprocessing is reduced to the realignment of individual 3D voxel spectra. As a result, the line broadening due to the field inhomogeneity over the sample's volume is reduced to the broadening by inhomogeneity within individual voxels. We compared this method with the resolution enhancement by window multiplication. We demonstrated, theoretically and experimentally, that in the presence of large, lower-order gradients, 3D CSI achieves better resolution enhancement with smaller sensitivity losses. An application of the method to a simple biological system is presented as well.     

Neural network processing for adaptive line enhancement

This paper describes the inverstigation devoted to establish suitable weights in a feed-forward neural network realizing the narrow-band filtering map in the case of adaptive line enhancement(ALE) by the utility of the optimum common learning rate back propagation (OCLR BP) algorithm. It is found that a feed-forward network with 64 linear input and output neurons, and 8 odd sigmoid neurons in the hidden layer, i.e. an (64→8→64) architecture, could establish the specific input-output function in the case of relatively low signal-to-noise radio. Only is an input signal consisting of mixed periodic and broad-band components available to the network system. After learning, both the \"fanning-in-connection patterns\", each of which consists of weights fanning into a hidden-neuron From all the outputs of input-neurons, and the \"fanning-out-connection patterns\", each of which consists of weights fanning out from a hidden-neuron to all the inputs of output-neurons, are tuned to the periodic signals. The nonline     

Lateral resolution enhancement with standing evanescent waves

A high-resolution fluorescence microscopy technique has been developed that achieves a lateral resolution of better than one sixth of the emission wavelength (FWHM). By use of a total-internal-reflection geometry, standing evanescent waves are generated that spatially modulate the excitation of the sample. An enhanced two-dimensional image is formed from a weighted sum of images taken at different phases and directions of the standing wave. The performance of such a system is examined through theoretical calculations of both the point-spread function and the optical transfer function.