Data hiding based on two-stage referencing for two-colour images

Abstract

Data hiding in two-colour images is difficult since 1 pixel requires only 1 bit representation and it is easy to detect for pixel distortion. In this paper, we describe a new data hiding method for two-colour images by two-stage referencing. The cover image is partitioned into n×n non-overlapping sub-blocks, and we calculate difference values by two stages for all pixels to find the suitable replacement pixel. The two-stage referencing is to obtain difference values that are different with the current pixel value for neighbouring pixels with n×n and (n+2)×(n+2). These two difference values are used to embed a secret bit on the sub-block. The experimental results show that the proposed method achieves a good visual quality for the stego-image.     

A secure authentication watermarking for halftone and binary images

An authentication watermark is a hidden data inserted into an image that can be used to detect any accidental or malicious alteration in the image. Many authentication‐watermarking techniques for continuous‐tone images are described in the literature, but only a quite small number of secure authentication watermarking techniques are available for binary/halftone images. This article proposes a simple solution for inserting a secure authentication watermark in binary/halftone images. It consists of choosing a set of pseudo‐random pixels in the image, clearing them, computing the message authentication code (or the digital signature) of the now‐cleared image, and inserting the resulting code into the selected random pixels. Dispersed‐dot halftone images watermarked by the proposed technique present better visual quality than do watermarked generic binary images. However, in practice, the visual degradation is hardly noticeable in either case. The proposed technique seems to be the only binary/halftone watermarking scheme that can detect even a single pixel alteration in the host image. It can be used with secret‐key or public‐key ciphers. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 147–152, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20018     

Pixel expansion-free grey-scale image sharing method

Abstract

In recent years, many visual secret sharing technologies have been proposed to protect the security of secret images (black and white, grey scale or colour images). In 2005, Lukac and Plataniotis used the concept of the Naor–Shamir method to propose a visual secret sharing technology for sharing secret grey-scale images based on bit-plane decomposition. Although their method can avoid pixel-value cutting problem, it still suffers from the pixel expansion problem. In this paper, we propose a new secret grey-scale image sharing method to improve this situation. In the proposed sharing image creation phase, two sharing images (one is grey-scale and the other is binary) are created and later shared by two protectors. The original secret image can be easily reconstructed if both sharing images are obtained. However, one cannot obtain the original secret image from each of the sharing images. Experimental results also show that the proposed method can effectively solve the pixel expansion problem.     

Dual image based reversible data hiding method using neighbouring pixel value differencing

Abstract

Dual image based reversible data hidings are recently proposed where dual copies of a cover image are used to embed the secret data. In this paper, a novel reversible data hiding method based on neighbouring pixel value differencing is proposed to increase embedding capacity with a good image quality. The mean value of neighbouring pixels and the difference values are used to decide the size of embedding bits per the sub-block. The receiver can extract the secret data and recover the cover image from dual stegoimages. The experimental results show that the proposed method has a higher capacity and still a good image quality where it could embed 767,922 bits and maintain 45.58 and 45.33?dB for dual images.     

采用图像融合技术的多模式人脸识别

利用图像融合技术实现了基于可见光图像和红外热图像相结合的多模式人脸识别,研究了两种图像在像素级和特征级的融合方法.在像素级,提出了基于小波分解的图像融合方法,实现了两种图像的有效融合.在特征级,采用分别提取两种识别方法中具有较好分类效果的前50%的特征进行特征级的融合.实验表明,经像素级和特征级融合后,识别准确率都较单一图像有很大程度的提高,并且特征级的融合效果明显优于像素级的融合.因此,基于图像融合技术的多模式人脸识别,有效的增加了图像的信息量,是提高人脸识别准确率的有效途径之一.     

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images.     

一种感兴趣区域防溢出数字水印算法

目的针对工程设计图纸、医学影像等存在大量边缘值像素点的图像在嵌入水印时容易出现像素值溢出的问题,文中提出一种基于感兴趣区域防溢出的水印方案。方法该方案使用改进后的区域生长法分割原始图像得到不规则形状的ROI,再通过填充像素值得到矩形水印嵌入区域,对该区域进行提升小波变换,并经过Arnold置乱,通过对水印嵌入区域的分块和扫描,跳过边缘像素值,使用奇偶映射的方法将制成QR码的水印信息嵌入至宿主图像。结果嵌入水印后图像边缘仍完整,像素值溢出现象得到大大改善,嵌入水印后图像峰值信噪比高于48db,水印不可见性高于一般水印嵌入方案,满足高精度图像对水印不可见度要求,且经过攻击后仍能实现水印的提取。结论该方案充分考虑到含有大面积背景和大量边缘像素值图像的特点,保证认证水印嵌入至图像关键ROI区域,在保证原图质量的基础上实现了防溢出的水印嵌入,经过攻击后提取的水印仍能够读取认证信息,在包装印刷工程中具有实际应用价值。     

A framework for moving object segmentation using dynamic background modelling and shadow suppression in complex wavelet domain

In this paper, a framework for dynamic background modelling and shadow suppression under rapidly changing illumination conditions for moving object segmentation in complex wavelet domain is proposed which deals with the problems of ghosts, object shadows, noise, object distortion in dynamic background changes. The proposed method consists of eight steps applied on given video frames which include: wavelet de-composition of frame using complex wavelet transform; use of change detection on detail coefficients; use of dynamic background modelling on approximate co-efficient; cast shadow suppression; use of soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction and finally using closing morphology operator. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for four datasets in terms of various performance measures. Experimental results demonstrate the efficacy of the proposed method is better in terms of relative foreground area measure, misclassification penalty, relative position based measure, normalised cross-correlation, normalised absolute error, peak signal-to-noise ratio and pixel classification based measure as compared to other standard methods.     

A dynamic threshold-based local mesh ternary pattern technique for biomedical image retrieval

Many content-based image retrieval techniques like local binary pattern (LBP), local ternary pattern (LTP), local mesh peak valley edge pattern (LMePVEP), local mesh ternary pattern (LMeTerP), etc. extract texture features of an image for retrieval purposes. These techniques use fixed threshold to encode the input image and selection of such threshold value is not guided, that is, a chosen threshold may not be optimal for all images in the database. Moreover the performance of these texture-based static threshold algorithms also decreases if the input images are noisy. In this paper, a dynamic threshold value-based local mesh ternary pattern method is proposed in which the threshold value is chosen from the neighborhood of a central pixel using median of all pixels. The proposed modification reduces the overall effect of noise component and thereby improves the average retrieval rate (ARR) and average retrieval precision (ARP) of the original technique. The proposed modified technique has been compared with five other image retrieval approaches to prove its worthiness - the original local mesh ternary pattern technique (LMeTerP), a local ternary pattern technique (LTP), a Best ensemble technique, a multi-label classification CNN model and a CNN-based model of the proposed approach using a VIA ELCAP lung database and an Emphysema database. An improvement of 3.92% in ARR and 2.53% in ARP is observed over the basic local mesh ternary pattern method. Further the proposed modification has been combined with CNN concept and its results have also been analyzed.     

一种采用多个小波基的图像融合去噪方法

提出了一种新的基于多个小波基的图像融合去噪方法.首先利用多个不同的小波基对含噪图像进行阈值去噪,得到多幅恢复图像.然后对这些图像采用小波融合方法进行融合.对于低频系数采用基于边缘的融合算法,在多幅恢复图像中选择最有可能是边缘的点加以保留;对于高频系数,采用了平均的融合算法.最后得到一幅去噪图像.实验结果表明,无论是在视觉效果上还是在峰值信噪比定量指标上该方法去噪效果均明显优于单一小波基去噪.     

A colour image retrieval scheme based on Z-scanning technique

Abstract

This paper proposes a new colour image retrieval scheme using Z-scanning technique for content-based image retrieval (CBIR). In recent years, the CBIR is a popular research topic for image retrieval. This paper proposes a scheme which employs the Z-scanning technique to extract directional intensity features for measuring the similarity between query and database images. In the multiple channel images, each colour channel can be processed individually or combined into a grey channel Y. In order to extract the features by Z-scanning technique from all images, each channel of all images must be divided into several N×N blocks. In each block, F pairs of pixels are scanned by a ‘Z’ direction to obtain the texture features. Each colour channel can be obtained an M×M Z-scanning co-occurrence matrix (ZSCM) for storing the probability of each relationship of all closest blocks. At the similarity measure stage, the ZSCMs of query image and database images are compared to measure their similarity. The experimental results show that the proposed scheme is beneficial for image retrieval when the images include the same texture or object. On the other hand, the proposed scheme also can get better retrieval results and more efficiency than colour correlogram (CC) technique for colour texture images. Another technique uses motif co-occurrence matrix (MCM) as the feature in similarity measurement. The experimental results show the proposed ZSCM can get better retrieval results and higher recall and precision values than the CC and MCM techniques for public image databases.     

Image fusion based on wavelet transform and gray-level features

Image fusion makes the fused image more reliable and intelligible, and more suitable for human vision and computer detection, classification, recognition and understanding. This paper proposes a pixel-level image fusion method for merging two source images of the same scene using wavelet transform and gray-level features (GLF). First, a three-level discrete two-dimensional wavelet transform is used to decompose the two source images into low-frequency image components and horizontal, vertical, and diagonal high-frequency components. Then, the spatial frequency correlation coefficient is used to determine the pixel fusion rule to apply to each of the low-frequency images, and the correlation coefficient of the GLF is used to determine the pixel fusion rule to apply to each of the high-frequency images. Finally, the fused image is reconstructed using inverse wavelet transform. The results of the experiments conducted indicate that the proposed method is more effective than relevant conventional methods.     

A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming

We present an intelligent technique for image denoising problem of gray level images degraded with Gaussian white noise in spatial domain. The proposed technique consists of using fuzzy logic as a mapping function to decide whether a pixel needs to be krigged or not. Genetic programming is then used to evolve an optimal pixel intensity‐estimation function for restoring degraded images. The proposed system has shown considerable improvement when compared both qualitatively and quantitatively with the adaptive Wiener filter, methods based on fuzzy kriging, and a fuzzy‐based averaging technique. Experimental results conducted using an image database confirms that the proposed technique offers superior performance in terms of image quality measures. This also validates the use of hybrid techniques for image restoration. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 224–231, 2007     

Exploitation of image coherency and topology preservation to accelerate the pixel mapping stage of SOM-based image quantisation

Abstract

SOM-based image quantisation requires a considerable amount of processing time even during the pixel mapping stage. Basically, a full search algorithm is employed to find a codeword, within a codebook, whose distance to the queried pixel is minimum. In this paper, we present a novel approach to accelerate the pixel mapping stage by utilisation of the spatial redundancy of pixels in the image and the inherent topological preservation nature of the resulting codebook. The experimental results confirm that the proposed approach outperforms ordinary solutions and is comparable to state-of-the-art solutions in terms of execution time. In addition, as the proposed approach does not require codebook sorting and a complex data structure with variable sizes, this simplifies its implementation and makes it feasible for hardware realisation.     

Deep underwater image enhancement through colour cast removal and optimization algorithm

ABSTRACT

Blue–green colour cast effect and low contrast are common problems suffered by deep underwater images. This paper introduces a new method which consists of two major steps: red channel correction based on green and blue channels (RCCGB), and simultaneous contrast stretching and mean pixel enhancement (SCSMPE). The RCCGB is designed to minimize the effect of blue–green illumination. This step considers the differences between the red channel and other channels in terms of total pixel values. The second major step, SCSMPE is specifically designed to perform contrast stretching and improve the mean pixel value simultaneously through particle swarm optimization (PSO). Based on the visual observation, the proposed method significantly reduces the effect of the blue–green colour cast and improves the image contrast. Furthermore, the average quantitative values for 300 underwater images also demonstrate the superiority of the proposed method.     

基于方差和显著性特征的超声图像分割方法研究

针对超声图像对比度小导致的影像上相邻灰度差别很小,人眼有时难于区分的问题,提出了一种基于方差和显著性特征的超声图像分割方法。首先提取图像中已知样本像素点的方差和显著性特征;然后利用支持向量机根据提取的样本像素点方差和显著性特征进行样本训练,得到分类模型;最后根据训练模型对整幅图像上的像素点进行分类,实现图像的有效分割。实验结果表明该方法针对超声图像的分割是有效的。     

Improving performance of wavelet-based image de-noising algorithm using complex diffusion process

Abstract

Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the de-noised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise.     

基于二值离散运算的自适应数字图像水印算法

论文提出了一种在小波变换域内基于二值离散运算的自适应数字图像水印算法.算法利用人眼视觉掩盖特性,根据要嵌入的伪随机数水印长度自适应地选择原始图像小波逼近子图中纹理信息丰富的图像子块,通过对这些图像子块系数的二值离散修改实现水印的嵌入,水印检测无需原始图像.由于水印的嵌入与检测是基于二值离散运算,所嵌入的水印具有很理想的鲁棒性,能免疫于常规图像处理方法的有限攻击.同时,嵌入的水印具有理想的隐蔽效果,完全满足水印不可觉察性要求.     

New surface flattening scheme and its application in the visualization of the human cortex unfolding

Abstract

It is a difficult problem to interpret the spatial relationship between different locations on the human cortical surface. The main reason for this difficulty is that the surface of the human cerebral cortex is highly folded and therefore much of it is obscured from view. In this paper, we present an efficient and distortion‐less flattening algorithm to unfold the cortical surface. Using the proposed technique, we can quickly visualize and precisely measure the cortical surface. Experimental results are shown to evaluate the usefulness of the proposed method.     

基于平滑卷积定位的圆形印鉴提取方法研究

利用圆形印鉴中心的五角星所在区域红色像素分布集中的特点,提出了一种基于平滑卷积定位的圆形印鉴提取算法.该算法首先将彩色票据图像转换为灰度图像,接着利用平滑卷积定位五角星区域中的一个像素点,以这个像素点邻域的平均灰度值作为阈值二值化票据图像,然后搜索圆形印鉴边框,计算圆心坐标和半径,从而实现印鉴的提取.同时,为了提高提取速度,通过适当减小卷积过程中图像的分辨率,优化了整个提取过程中时间复杂度最高的平滑卷积算法.实验结果表明,这种方法能够在复杂背景下有效地提取出圆形印鉴,在经过算法优化后,提取速度提高了近12倍.