Perceptual assessment of demosaicing algorithm performance

Demosaicing is an important part of the image-processing chain for many digital color cameras. The demosaicing operation converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a full-color image. In this paper, we report the results of two perceptual experiments that compare the perceptual quality of the output of different demosaicing algorithms. In the first experiment, we found that a Bayesian demosaicing algorithm produced the most preferred images. Detailed examination of the data, however indicated that the good performance of this algorithm was at least in part due to the fact that it sharpened the images while it demosaiced them. In a second experiment, we silenced image sharpness as a factor by applying a sharpening algorithm to the output of each demosaicing algorithm. The optimal amount of sharpening to be applied to each image was chosen using the results of a preliminary experiment. Once sharpness was equated in this way, an algorithm developed by Freeman based on bilinear interpolation combined with median filtering, gave the best results. An analysis of our data suggests that our perceptual results cannot be easily predicted using an image metric     

Joint demosaicing and denoising.

The output image of a digital camera is subject to a severe degradation due to noise in the image sensor. This paper proposes a novel technique to combine demosaicing and denoising procedures systematically into a single operation by exploiting their obvious similarities. We first design a filter as if we are optimally estimating a pixel value from a noisy single-color (sensor) image. With additional constraints, we show that the same filter coefficients are appropriate for color filter array interpolation (demosaicing) given noisy sensor data. The proposed technique can combine many existing denoising algorithms with the demosaicing operation. In this paper, a total least squares denoising method is used to demonstrate the concept. The algorithm is tested on color images with pseudorandom noise and on raw sensor data from a real CMOS digital camera that we calibrated. The experimental results confirm that the proposed method suppresses noise (CMOS/CCD image sensor noise model) while effectively interpolating the missing pixel components, demonstrating a significant improvement in image quality when compared to treating demosaicing and denoising problems independently.     

Multiframe demosaicing and super-resolution of color images.

In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteron estimation technique by minimizing a multiterm cost function. The L1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.     

Linear demosaicing inspired by the human visual system.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.     

Adaptive snakes using the EM algorithm.

Deformable models (e.g., snakes) perform poorly in many image analysis problems. The contour model is attracted by edge points detected in the image. However, many edge points do not belong to the object contour, preventing the active contour from converging toward the object boundary. A new algorithm is proposed in this paper to overcome this difficulty. The algorithm is based on two key ideas. First, edge points are associated in strokes. Second, each stroke is classified as valid (inlier) or invalid (outlier) and a confidence degree is associated to each stroke. The expectation maximization algorithm is used to update the confidence degrees and to estimate the object contour. It is shown that this is equivalent to the use of an adaptive potential function which varies during the optimization process. Valid strokes receive high confidence degrees while confidence degrees of invalid strokes tend to zero during the optimization process. Experimental results are presented to illustrate the performance of the proposed algorithm in the presence of clutter, showing a remarkable robustness.     

自适应数字水印嵌入算法

研究基于小波变换域的数字水印的嵌入对策和检测方法,实现了数字水印自适应地嵌入列原始图像离散小波变换域的中频子带图像中。使用该算法嵌入的数字水印具有很好的隐蔽性,视觉上感觉不到对原始图像的影响,嵌入的数字水印具有理想的鲁棒性,常规的图像处理方法对其影响很小,实验证明了该算法的有效性和安全性。     

自适应多层快速多极子算法及其并行算法

在多层快速多极子算法的基础上提出了一种改进的电大问题电磁散射快速算法及其并行算法.算法中将基函数和权函数分别用不同空间位置上的点源函数展开,使各部分的积分计算得到大大简化,所有转移过程可由快速傅里叶变换计算完成,同时还给出了该算法的并行化算法.数值结果说明了算法的有效性.     

Adaptive MLSDE using the EM algorithm

The theory of adaptive sequence detection incorporating estimation of channel and related parameters is studied in the context of maximum-likelihood (ML) principles in a general framework based on the expectation and maximization (EM) algorithm. A generalized ML sequence detection and estimation (GMLSDE) criterion is derived based on the EM approach, and it is shown how the per-survivor processing and per-branch processing methods emerge naturally from GMLSDE. GMLSDE is developed into a real time detection/estimation algorithm using the online EM algorithm with coupling between estimation and detection. By utilizing Titterington's (1984) stochastic approximation approach, different adaptive ML sequence detection and estimation (MLSDE) algorithms are formulated in a unified manner for different channel models and for different amounts of channel knowledge available at the receiver. Computer simulation results are presented for differentially encoded quadrature phase-shift keying in frequency flat and selective fading channels, and comparisons are made among the performances of the various adaptive MLSDE algorithms derived earlier     

Adaptive resampling algorithm for image zooming

Applying an interpolation function indiscriminately to an image, to resample it, will generally result in aliasing, edge blurring and other artefacts. The authors present an adaptive resampling algorithm for zooming up images. The algorithm is based on analysing the local structure of the image and applying a near optimal and least time-consuming resampling function will preserve edge locations and their contrast. This is done by segmenting the image dynamically into homogeneous areas, as it is scanned or received. Based on the location of the pixel to be computed (whether it is within a homogenous area, is on its edge or is an isolated feature), interpolation, extrapolation or pixel replication is chosen. The algorithm performance, from both a quality and a computational complexity aspect, are compared to different methods and functions previously reported in the literature. The advantage of the algorithm is quite apparent at edges and for large zooming factors     

Adaptive tracking algorithm for mobile communications

The authors propose a new adaptive tracking algorithm which has been shown by computer simulations to outperform the conventional decision feedback equalisation algorithm. Basically, a cost function is formed by generating more than one reference signal based on the time recursive phase characteristic of an MSK signal     

Adaptive step-size minor component extraction algorithm

An information criterion is introduced for extracting the minor component associated with the covariance matrix of an input process. An adaptive stepsize learning algorithm (ASMCEX) is proposed. The analysis shows that the ASMCEX algorithm has an equilibrium point. The simulations show that the ASMCEX algorithm can improve the convergence speed significantly     

一种基于特征的自适应水印算法

基于图像多尺度空间理论和扩频机制以及视觉感知模型的特性,给出一种基于特征的数字图像水印算法。算法采用尺度不变特征变换算子提取图像的局部不变特征区域,根据信噪比特点自适应选取水印嵌入强度因子,将一幅二值水印图像嵌入到原始图像中,实现了数字水印的不可感知性和鲁棒性。仿真结果表明,该算法对添加噪声、压缩、旋转等常见的图像处理攻击具有较强的鲁棒性。     

自适应机动地形匹配算法

文章首先提出了基准数据带的自适应构造法,然后以它和平均绝对差算法为基础,形成了一种新的算法,称为自适应机动地形匹配算法。计算机的仿结果表明：该虎法和通常所用的算法相比,具有较高的捕获概率和定位精度,特别是在低信噪比的条件下,性能改善程序更为显著。     

基于Otsu算法与直方图分析的自适应Canny算法的改进

Canny算子在边缘检测领域被广泛应用,但传统的Canny算子需要人为设定梯度阈值和合适的高斯标准差,因此自动化程度不高。为解决Canny算子的自适应性,发展了包括Otsu算法在内的自适应算法。但传统的Otsu算法存在定位精度不高,易受高斯滤波标准差影响等问题。在Otsu算法的基础上,通过对高斯滤波后梯度图像的分析与研究,对最大类间方差进行改进,从而提高自适应算法的稳定性。将改进后的类间方差与类内方差的比值作为阈值选取的评价标准,从而有效提高了自适应算法的定位精度。仿真表明,所提出的改进算法能有效提高Canny算法的定位精度和鲁棒性。     

Adaptive block matching algorithm for video compression

A simple adaptive block matching algorithm for video coding is presented. In the proposed algorithm, a matching criterion called bit-plane matching is proposed to sort out a number of blocks with unlikely directive information to the test block from the search window. An adaptive searching method is developed to further improve the computational effectiveness of the system. In this searching method the search origin, search range and pixel subsampling ratio can be adjusted according to the motion content of the block. Simulation results show that the proposed algorithm achieves MSE performance to the full-search and about 92-95% savings in computation     

基于自检测的自适应一致表决算法

为了解决自适应大数表决算法无法容忍表决周期发生瞬时错误的问题,提出了基于自检测的自适应一致表决算法。该算法通过插入检测代码实时搜集瞬时错误信息,进而屏蔽发生瞬时错误的软件冗余模块参与表决,并将各软件冗余模块历史记录信息有效地应用到表决系统。在此算法的基础上,设计了能实现上述功能的表决系统结构图。最后通过仿真实验证明了所提算法的有效性。     

Adaptive fast encoding algorithm for vector quantisation

A new fast encoding algorithm is developed for vector quantisation (VQ). The proposed algorithm takes the topological structure of the codebook into account and controls the search space adaptively. Simulation results demonstrate that while the performance of the proposed algorithm is the same as that using an exhaustive search, it is more efficient than some of the other related fast VQ encoding algorithms     

话音通信中的数据自适应隐藏算法

基于话音通信的PCM量化编码,导出了数据嵌入强度因子的选取准则,给出了两种自适应选取的实现策略,进而提出了根据载体信号的幅度以及信道的噪声强度自适应调整的数据隐藏算法.隐藏信息为二进制比特数据时,仿真实验结果表明该自适应隐藏方法具有良好的隐蔽信息传输性能.     

基于Kalman预测的自适应De-interlace算法

本文提出一种基于Kalman预测的自适应De-interlace算法.它包括运动块检测、自适应的运动估计以及对运动块的运动补偿和对静止块的场复制等模块.其中,运动块检测用了连续的4场图像,能够更精确地实现运动检测;对检测到的运动块用Kalman滤波进行自适应地运动估计,并根据估计到的运动矢量进行运动补偿;对静止块,用场复制法进行De-interlace处理.该算法在de-interlace性能接近全搜索算法的前提下,运算量降低了三个数量级.     

基于分块DCT的自适应水印算法

近几年来,随着因特网的普及和发展,数字图像的版权问题越来虎引起人们的关注,其中数字水印作为保护版权的一种有效手段得到了广泛的注意。文中提出一种自适应水印算法,并比较了Ｇａｕｓｓ和Ｌｅｇｅｎｄｒｅ两种不同同的随机阵列的效果。该方法将随机阵列添加到图像子块的ＤＣＴ次低频系数中去,并根据ＨＶＳ的特点进行自适应处理和视觉掩盖。实验结果表明,实现的水印具有不可见性,而且对于常见图像压缩和噪声干扰具有很好的健