基于双向反馈的自适应误差扩散半色调算法

误差扩散算法是图像进行半色调处理的主要方法,广泛地应用于各种二值图像处理设备.针对传统误差扩散中的"龟纹"和"伪轮廓"现象,在基于双向反馈误差扩散算法的基础上,提出一种双向反馈自适应误差扩散半色调算法,它不仅在误差扩散后的高频和低频成分中加入了中间频带,使图像更趋于均匀和柔和,并且通过自适应的方法,随图像特性动态修改误差扩散滤波器系数,最大程度地克服由于固定系数扩散所产生的方向性纹理缺陷,使处理后的二值图像更加充分地表现原图像信息.采用激光雕刻设备证明了该算法的有效性.     

基于双向反馈及HVS模型的误差扩散算法

针对传统误差扩散中的"龟纹"和"伪轮廓"现象,在基于标准误差扩散算法的基础上,提出一种基于双向反馈的误差扩散半色调算法.它不仅在误差扩散后的高频和低频成分中依据HVS模型加入了中间频带,使图像更趋于均匀和柔和,并且根据领域图像灰度特征和HVS特征动态修改误差扩散滤波器系数,尽可能的克服了由于固定系数扩散所产生的方向性纹理缺陷.     

基于视觉特性的灰度级自适应盲水印算法

提出了一种基于小波变换和视觉感知特性的灰度级自适应盲水印算法。该算法利用视觉系统的背景亮度、纹理和边界掩盖特性把载体图像的小波系数分为18类；在对256级灰度水印图像压缩后。依据载体图像小波系数的分类，以不同强度嵌入到载体中。该算法在抽取水印时不需要原始载体图像和原始水印。实验结果表明此算法可有效地隐藏灰度水印图像，并有较好的顽健性，能抵抗裁剪、JPEG压缩、加噪、图像增强、滤波等多种处理。     

基于自适应SUSAN各向异性扩散的红外弱小目标检测

针对复杂背景下红外弱小目标提取困难的问题,提出了一种自适应SUSAN各向异性扩散的红外弱小目标检测算法。该算法结合SUSAN边缘检测算子与各向异性扩散,形成新的扩散方程对红外图像进行背景预测,与原图像差分后实现弱小目标检测。为使算法具备自适应能力,提出SUSAN边缘检测器灰度差阈值的自适应设定方法,采用绝对偏差中值算子作为其扩散系数。实验结果表明,该算法能够有效滤除复杂图像背景,大幅提升信噪比,同时保留目标大小。     

视觉传感网络中身份特征自适应识别算法改进

在对视觉传感网络中身份特征进行识别时,容易受到人脸表情、光照条件及遮挡等干扰,降低了身份特征识别精度.提出了一种基于改进最小灰度差树的身份特征自适应识别算法.对待识别图像进行灰度处理后,利用最小灰度差数增强待识别图像的质量;定义基于灰度的代价函数,获取待识别人脸图像和指定人脸图像对应的各灰度对的匹配代价,建立最小灰度差树模型,计算两幅图像相似度后,直接采用最近邻匹配算法获取和视觉传感网络注册图库中最小匹配代价对应的图像身份,将其看作待识别身份,实现视觉传感网络中身份特征自适应识别.仿真实验结果表明,所提算法具有很高的身份识别精度.     

一种改进的贝叶斯小波阈值图像去噪算法

小波阈值去噪作为图像降噪领域的一项重要技术一直受到广泛应用.在贝叶斯阈值的基础上提出一种改进的贝叶斯阈值去噪算法,该阈值是在贝叶斯框架中得出的,在小波系数上使用的优先级是在图像处理应用中广泛使用的广义高斯分布(Generalized Gaussian Distribution,GGD).该阈值算法适用于每个子带,取决于数据驱动自适应参数估计,通过判断阈值周围的小波系数是否含有噪声的模糊性,从而对该模糊区域通过自适应算法确定小波系数的保留程度.实验结果表明,该方法比原方法在主观视觉效果上得到了明显的改善,较好地保持图像边缘细节,并且均方误差(Mean Square Error,MSE)较其他阈值算法有所减少,信噪比(Signal-Noise Ratio,SNR)较其他阈值算法有所提升.     

基于视觉感知和边缘保持的光照不变人脸识别

提出一种具有视觉感知特性和边缘保持特性的光照不变人脸识别方法.方法在各向异性扩散算法基础上引入视觉感知机制,提出具有视觉感知特性的图像梯度替代传统的图像空间梯度,使算法更符合人类视觉系统特性;同时,考虑到传统各向异性扩散算法采用的传递系数受参数影响较大,易产生明显的边缘锐化现象,提出一种新的传递系数,该系数不受参数影响,能够始终保持良好的边缘保持特性.新方法所获的光照不变人脸图像保持了良好的边缘,并极大程度上消除了光晕和白斑现象.在EYaleB和CMU PIE人脸图像库上的实验验证了该方法的有效性.     

脊波变换域的一种自适应盲水印算法

为了克服水印鲁棒性和图像视觉质量之间的矛盾,提出了一种脊波变换域自适应盲水印算法.利用脊波变换对图像特征的稀疏表示特性,找出各区域块中视觉重要信息的位置,并将水印嵌入其中.算法中水印嵌入强度依据图像块的内容属性变化,采用了系数可容忍的最大误差作为水印嵌入强度因子,较好地解决了水印鲁棒性和图像视觉质量之间的矛盾.实验结果表明,该算法能够抵抗JPEG压缩、加噪和任意裁剪等攻击,具有很强的鲁棒性,是一种自适应盲水印算法.     

轮廓波域内局部对比度增强的彩色图像灰度化算法

为了更好地保留原彩色图像的局部对比度,获得感知准确的灰度图像,提出了一种轮廓波域内局部对比度增强的灰度化算法。首先在CIE Lab色彩空间中利用共轭梯度算法优化目标函数,求取全局映射函数参数,得到初步灰度化图像;然后采用轮廓波变换对彩色图像和初步灰度化图像进行多尺度多方向分解,利用局部色彩对比度比值对方向细节图像进行对比度增强,应用轮廓波逆变换得到增强后的细节图像;最后将初步灰度化图像与增强后的细节图像进行叠加,得到灰度化图像。对COLOR250和ˇCadik图像集的实验结果表明,本文提出的算法优于已有文献的一些典型灰度化算法,能够有效保留原始图像的对比度与结构信息,灰度化图像视觉感知自然,主客观评价结果均为最优。     

基于HVS特性的信息隐藏算法改进

为了提高原信息隐藏算法的不可感知性和扩展其隐藏容量,根据人类视觉系统（HVS）相关知识,从提高信息隐藏容量的角度出发,根据人眼对图像不同区域的敏感程度,充分利用人类视觉系统的方向特性、纹理特性和亮度特性对原算法进行改进。并用文本和图像文件分别作为秘密信息,couPle．bmp灰度图像为载体图像,进行算法对比试验。实验表明改进后算法缓解了信息隐藏系统各指标之间存在的矛盾,提高了秘密信息隐藏率和抵抗攻击的能力,进一步增强了隐藏系统的安全性,为大容量信息隐藏提供了一种思路。     

Adaptive error diffusion and its application in multiresolutionrendering

Error diffusion is a procedure for generating high quality bilevel images from continuous-tone images so that both the continuous and halftone images appear similar when observed from a distance. It is well known that certain objectionable patterning artifacts can occur in error-diffused images. Here, we consider a method for adjusting the error-diffusion filter concurrently with the error-diffusion process so that an error criterion is minimized. The minimization is performed using the least mean squares (LMS) algorithm in adaptive signal processing. Using both raster and serpentine scanning, we show that such an algorithm produces better halftone image quality compared to traditional error diffusion with a fixed filter. Based on the adaptive error-diffusion algorithm, we propose a method for constructing a halftone image that can be rendered at multiple resolutions. Specifically, the method generates a halftone from a continuous tone image such that if the halftone is down-sampled, a binary image would result that is also a high quality rendition of the continuous-tone image at a reduced resolution. Such a halftone image is suitable for progressive transmission, and for cases where rendition at several resolutions is required. Cases for noninteger scaling factors are also considered.     

Model-based digital halftoning

Digital halftoning is the process of generating a pattern of pixels with a limited number of colors that, when seen by the human eye, is perceived as a continuous-tone image. Digital halftoning is used to display continuous-tone images in media in which the direct rendition of the tones is impossible. The most common example of such media is ink or toner on paper, and the most common rendering devices for such media are, of course, printers. Halftoning works because the eye acts as a spatial low-pass filter that blurs the rendered pixel pattern, so that it is perceived as a continuous-tone image. Although all halftoning methods rely at least implicitly, on some understanding of the properties of human vision and the display device, the goal of model-based halftoning techniques is to exploit explicit models of the display device and the human visual system (HVS) to maximize the quality of the displayed images. Based on the type of computation involved, halftoning algorithms can be broadly classified into three categories: point algorithms (screening or dithering), neighborhood algorithms (error diffusion), and iterative algorithms [least squares and direct binary search (DBS)]. All of these algorithms can incorporate HVS and printer models. The best halftone reproductions, however, are obtained by iterative techniques that minimize the (squared) error between the output of the cascade of the printer and visual models in response to the halftone image and the output of the visual model in response to the original continuous-tone image.     

Color error-diffusion halftoning

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.     

Hardcopy image barcodes via block-error diffusion.

Error diffusion halftoning is a popular method of producing frequency modulated (FM) halftones for printing and display. FM halftoning fixes the dot size (e.g., to one pixel in conventional error diffusion) and varies the dot frequency according to the intensity of the original grayscale image. We generalize error diffusion to produce FM halftones with user-controlled dot size and shape by using block quantization and block filtering. As a key application, we show how block-error diffusion may be applied to embed information in hardcopy using dot shape modulation. We enable the encoding and subsequent decoding of information embedded in the hardcopy version of continuous-tone base images. The encoding-decoding process is modeled by robust data transmission through a noisy print-scan channel that is explicitly modeled. We refer to the encoded printed version as an image barcode due to its high information capacity that differentiates it from common hardcopy watermarks. The encoding/halftoning strategy is based on a modified version of block-error diffusion. Encoder stability, image quality versus information capacity tradeoffs, and decoding issues with and without explicit knowledge of the base image are discussed.     

连续图像数字半调处理及性能分析

本文阐述了连续图像数字半调处理的基本理论,包括有序抖动数字半调处理、误差扩散数字半调处理,并对2种处理进行了性能分析。     

Halftone to continuous-tone conversion of error-diffusion codedimages

Considers the problem of reconstructing a continuous-tone (contone) image from its halftoned version, where the halftoning process is done by error diffusion. The authors present an iterative nonlinear decoding algorithm for halftone-to-contone conversion and show simulation results that compare the performance of the algorithm to that of conventional linear low-pass filtering. They find that the new technique results in subjectively superior reconstruction. As there is a natural relationship between error diffusion and SigmaDelta modulation, the reconstruction algorithm can also be applied to the decoding problem for SigmaDelta modulators.     

A dual interpretation for direct binary search and its implicationsfor tone reproduction and texture quality

The direct binary search (DBS) algorithm employs a search heuristic to minimize the mean-squared perceptually filtered error between the halftone and continuous-tone original images. Based on an efficient method for evaluating the effect on the mean squared error of trial changes to the halftone image, we show that DBS also minimizes in a pointwise sense the absolute error under the same visual model, but at twice the viewing distance associated with the mean-squared error metric. This dual interpretation sheds light on the convergence properties of the algorithm, and clearly explains the tone bias that has long been observed with halftoning algorithms of this type. It also demonstrates how tone bias and texture quality are linked via the scale parameter, the product of printer resolution and viewing distance. Finally, we show how the tone bias can be eliminated by tone-correcting the continuous-tone image prior to halftoning it.     

一种基于H.264/AVC 的数据自适应不平等保护策略

根据H.264标准的数据分类功能,提出了一种自适应的数据不平等保护策略.该策略能根据反馈回来的信道状态和解码信息,自适应地调整H.264视频码流各分类区的信道码率.试验结果显示,该策略能有效地提高视频码流的误码鲁棒性.     

Context-based, adaptive, lossless image coding

We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. The CALIC puts heavy emphasis on image data modeling. A unique feature of the CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics. The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, the CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use. The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts