基于HSV颜色空间的改进的多尺度Retinex算法

针对带颜色恢复的多尺度Retinex算法在最后输出的图像上存有重叠的问题,提出了一种改进的子频带分解的Retinex算法,该算法不仅能增强亮点中的细节,也能增强在阴影中的细节.由于RGB这三种颜色之间有很强的关系,而HSV这三者之间没有很大的关联,能够较好的反映人对色彩的感知.实验结果表明,与基于RGB空间的多尺度Retinex算法相比,改进的基于HSV的算法更有效的增强图像在亮点和阴影中的细节,颜色更接近于原图.     

一种色彩改进型Retinex彩色图像增强方法

提出了一种色彩改进型Retinex彩色图像增强方法.对于曝光量不足的彩色图像,采用多尺度Retinex算法增强后,在亮度平均值附近以1倍标准差进行截取,拉伸后的图像亮度与对比度的乘积较大,再采用Brovey彩色变换方法,以原图像各像素中红绿蓝所占的成份比例进行运算.由色彩改进型Retinex彩色图像增强方法增强后得到的图像信息熵是直接使用Retinex算法的1.019±0.002倍.     

典型场景彩色融合图像颜色协调性的主观评价

彩色夜视技术是当前夜视技术发展的前沿,但如何实现对彩色夜视图像质量的客观评价仍然是当前困扰人们的问题.针对北京理工大学高绍姝提出的颜色协调性模型主观评价实验人数不足的问题,按照国际视觉实验标准重新设计并进行了颜色协调性的主观视觉评价实验,主观评价测试的彩色融合图像分成绿色植物、海天和城镇建筑物三类典型场景,主观实验人数提高到52人;对主观评价实验结果的回归分析结果表明:高绍姝提出的颜色协调性评价模型在三类场景与主观评价结果均具有较好的一致性,明显优于其他评价模型,可以有效地对不同场景彩色融合图像的颜色协调性进行客观评价.     

Noise reduction using multiscale bilateral decomposition for digital color images

In this paper, we propose a noise reduction algorithm for digital color images using a nonlinear image decomposition approach. Most existing noise reduction methods do not adequately consider spatial correlation of color noise in digital color images. Color noise components in color images captured by digital cameras are observed as irregular grains with various sizes and shapes, which are spatially randomly distributed. We use a modified multiscale bilateral decomposition to effectively separate signal and mixed-type noise components, in which a noisy input image is decomposed into a base layer and several detail layers. A base layer contains strong edges, and most of noise components are contained in detail layers. Noise components in detail layers are reduced by an adaptive thresholding function. We obtain a denoised image by combining a base layer and noise-reduced detail layers. Experimental results show the effectiveness of the proposed algorithm, in terms of both the peak signal-to-noise ratio and visual quality.     

Personal communications networks bridging the gap between cellularand cordless phones

Today's cellular radiotelephone systems currently serve some 12 million subscribers but at average costs of $70/month for service, cellular remains a business, not a consumer, service. On the other hand, cordless phones are already a consumer product in over 40% of US households and annual sales of new cordless phones are already greater than sales of regular corded phones. Personal Communications Networks (PCN) providing Personal Communications Services (PCS) are designed to bridge the gap between expensive public cellular and private cordless services. In this paper we explore PCN/PCS topics including: a definition of the service, identification of the underlying technologies, and discussion of tradeoffs between the technologies     

A non-parametric depth modification model for registration between color and depth images

Multidimensional Systems and Signal Processing - Despite its most popularity among all depth cameras in the computer vision applications, the Microsoft Kinect sensor suffers from low depth...     

A hierarchical Markovian model for multiscale region-based classification of vector-valued images

We propose a new classification method for vector-valued images, based on: 1) a causal Markovian model, defined on the hierarchy of a multiscale region adjacency tree (MRAT), and 2) a set of nonparametric dissimilarity measures that express the data likelihoods. The image classification is treated as a hierarchical labeling of the MRAT, using a finite set of interpretation labels (e.g., land cover classes). This is accomplished via a noniterative estimation of the modes of posterior marginals (MPM), inspired from existing approaches for Bayesian inference on the quadtree. The paper describes the main principles of our method and illustrates classification results on a set of artificial and remote sensing images, together with qualitative and quantitative comparisons with a variety of pixel-based techniques that follow the Bayesian-Markovian framework either on hierarchical structures or the original image lattice.     

A high-quality fixed-length compression scheme for color images

We present a new compression method which compresses 8×8 picture blocks by fixed-length codewords. The compression operation is performed on the discrete cosine transforms, DCT, of each block. As a result, our method combines the distinct advantage of being fixed-length with the high image quality obtained by the DCT based compression methods. Our method has excellent error-resistance characteristics since it does not have the synchronization and error propagation problems inherent in variable-length coding methods     

A novel approach for coding color quantized images

An approach to the lossy compression of color images with limited palette that does not require color quantization of the decoded image is presented. The algorithm is particularly suited for coding images using an image-dependent palette. The technique restricts the pixels of the decoded image to take values only in the original palette. Thus, the decoded image can be readily displayed without having to be quantized. For comparable quality and bit rates, the technique significantly reduces the decoder computational complexity.     

一种鲁棒的彩色图像多功能水印算法

首先转换彩色图像的颜色空间抽取相应的灰度图像,再对灰度图像进行提升小波分解,在分解过程中利用图像压缩操作,引起近半数的小波系数增大,另一半系数减小,基于系数增减特征实现版权水印和消费者数字指纹的同时嵌入,从而获得版权认证和盗版追踪的双重功能。仿真实验表明该算法具有良好的鲁棒性和不可感知性。     

Saliency detection for panoramic landscape images of outdoor scenes

Saliency detection has been researched for conventional images with standard aspect ratios, however, it is a challenging problem for panoramic images with wide fields of view. In this paper, we propose a saliency detection algorithm for panoramic landscape images of outdoor scenes. We observe that a typical panoramic image includes several homogeneous background regions yielding horizontally elongated distributions, as well as multiple foreground objects with arbitrary locations. We first estimate the background of panoramic images by selecting homogeneous superpixels using geodesic similarity and analyzing their spatial distributions. Then we iteratively refine an initial saliency map derived from background estimation by computing the feature contrast only within local surrounding area whose range and shape are changed adaptively. Experimental results demonstrate that the proposed algorithm detects multiple salient objects faithfully while suppressing the background successfully, and it yields a significantly better performance of panorama saliency detection compared with the recent state-of-the-art techniques.     

A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images

Ultrasonic measurements of human carotid and femoral artery walls are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the interobserver variability and inefficiency. In this paper, we present a new automated method which reduces these problems. By applying a multiscale dynamic programming (DP) algorithm, approximate vessel wall positions are first estimated in a coarse-scale image, which then guide the detection of the boundaries in a fine-scale image. In both cases, DP is used for finding a global optimum for a cost function. The cost function is a weighted sum of terms, in fuzzy expression forms, representing image features and geometrical characteristics of the vessel interfaces. The weights are adjusted by a training procedure using human expert tracings. Operator interventions, if needed, also take effect under the framework of global optimality. This reduces the amount of human intervention and, hence, variability due to subjectiveness. By incorporating human knowledge and experience, the algorithm becomes more robust. A thorough evaluation of the method in the clinical environment shows that interobserver variability is evidently decreased and so is the overall analysis time. We conclude that the automated procedure can replace the manual procedure and leads to an improved performance.     

A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images

This paper introduces a new multiscale speckle reduction method based on the extraction of wavelet interscale dependencies to visually enhance the medical ultrasound images and improve clinical diagnosis. The logarithm of the image is first transformed to the oriented dual-tree complex wavelet domain. It is then shown that the adjacent subband coefficients of the log-transformed ultrasound image can be successfully modeled using the general form of bivariate isotropic stable distributions, while the speckle coefficients can be approximated using a zero-mean bivariate Gaussian model. Using these statistical models, we design a new discrete bivariate Bayesian estimator based on minimizing the mean square error (MSE). To assess the performance of the proposed method, four image quality metrics, namely signal-to-noise ratio, MSE, coefficient of correlation, and edge preservation index, were computed on 80 medical ultrasound images. Moreover, a visual evaluation was carried out by two medical experts. The numerical results indicated that the new method outperforms the standard spatial despeckling filters, homomorphic Wiener filter, and new multiscale speckle reduction methods based on generalized Gaussian and symmetric alpha-stable priors.     

A fuzzy noise reduction method for color images.

A new fuzzy filter is presented for the reduction of additive noise for digital color images. The filter consists of two subfilters. The first subfilter computes fuzzy distances between the color components of the central pixel and its neighborhood. These distances determine in what degree each component should be corrected. All performed corrections preserve the color component distances. The goal of the second subfilter is to correct the pixels where the color components differences are corrupted so much that they appear as outliers in comparison to their environment. Experimental results show the feasibility of the proposed approach. We compare with other noise reduction methods by numerical measures and visual observations. We also illustrate the performance of the proposed method as preprocessing step for edge detection.     

A color map for effective black-and-white rendering of color-scale images

Default color maps on color images often reproduce to confusing grayscale images. The proposed color map maintains an aesthetically pleasing color image that automatically reproduces to a monotonic grayscale with discrete, quantifiable saturation levels.     

A fully automatic one-scan adaptive zooming algorithm for color images

We present an interpolation algorithm for adaptive color image zooming. The algorithm produces the magnified image in one scan of the input image, and is fully automatic since does not involve any a priori fixed threshold. Given any integer zooming factor n, each pixel of the input image generates an n×n block of pixels in the zoomed image. For the currently visited pixel of the input image, the pixels of its associated block are first assigned tentative values, which are then adaptively updated before building the next block. The method is suggested for RGB images, but can equally be employed in other color spaces. Peak signal to noise ratio (PSNR) and Structural SIMilarity (SSIM) are used to evaluate the performance of the algorithm.     

Advantages of multiscale product filters for dynamic range compression in images

A multiscale product filter (MSPF) structure is proposed for use in digital image enhancement. The objective for this type of image processing is to mitigate the effect of uneven illumination on perceived image quality, a process known as dynamic range compression (DRC). The properties of the MSPF for DRC are investigated, a theoretical model is presented, and this model is used to predict the performance of the filter under step changes in illumination such as those caused by shadows. The advantage of the MSPF is that it provides a relatively rapid response for transitions from shadow regions to well-lit regions. This property is verified using random Mondrian textures with simulated illumination changes. The estimated illumination profile is compared with that used to generate the synthetic image using the Pearson's correlation coefficient. The improved performance of the MSPF is also confirmed by the mitigation of shadow artefacts in real images     

Directional multiscale modeling of images using the contourlet transform.

The contourlet transform is a new two-dimensional extension of the wavelet transform using multiscale and directional filter banks. The contourlet expansion is composed of basis images oriented at various directions in multiple scales, with flexible aspect ratios. Given this rich set of basis images, the contourlet transform effectively captures smooth contours that are the dominant feature in natural images. We begin with a detailed study on the statistics of the contourlet coefficients of natural images: using histograms to estimate the marginal and joint distributions and mutual information to measure the dependencies between coefficients. This study reveals the highly non-Gaussian marginal statistics and strong interlocation, interscale, and interdirection dependencies of contourlet coefficients. We also find that conditioned on the magnitudes of their generalized neighborhood coefficients, contourlet coefficients can be approximately modeled as Gaussian random variables. Based on these findings, we model contourlet coefficients using a hidden Markov tree (HMT) model with Gaussian mixtures that can capture all interscale, interdirection, and interlocation dependencies. We present experimental results using this model in image denoising and texture retrieval applications. In denoising, the contourlet HMT outperforms other wavelet methods in terms of visual quality, especially around edges. In texture retrieval, it shows improvements in performance for various oriented textures.     

A clustering-based color model and integral images for fast object tracking

The paper presents a clustering-based color model and develops a fast algorithm for object tracking. The color model is built upon K-means clustering, by which the color space of the object can be partitioned adaptively and the histogram bins can be determined accordingly. In addition, in each bin the multi-channel gray level is modelled as Gaussian distribution. Defined in this way the color model can describe accurately the color distribution with very little bins. To evaluate similarity between the reference model and the candidate model, a similarity measure based on Bhattacharrya distance is introduced and its simplified form is derived under assumption that in each bin distribution of gray level in different channel is independent of each other. Motivated by the paper of Viola and Jones, the Integral Images for computation of histogram, mean and variance are proposed, with which the similarity measure can be evaluated at negligible computational cost. Thus, exhaustive search is made efficiently for object localization which guarantees the global maximum be achieved. Comparisons with the well-known mean shift algorithm demonstrate that the proposed algorithm has better performance while having the same (or less) computational cost.