Robust and transparent watermarking scheme for colour images

In this study, a robust and transparent watermarking scheme for colour images is proposed. The colour features for the human visual system are utilised to design the colour watermarking scheme. Through the exploitation of the perceptual redundancy of colour images, the proposed watermarking scheme is perceptually tuned to embed and detect the watermark in the perceptually significant sub-bands of luminance and chrominance components of colour images in the wavelet domain. The employment of the uniformity in the uniform colour space and the masking effect mainly due to local variations in luminance magnitude leads to that the perceptual redundancy of colour images can be measured. By using the estimated perceptual redundancy in the form of error visibility thresholds of wavelet coefficients of the colour image, high strength watermarks are invisibly embedded into coefficients of the host colour image for resisting compression and malicious attacks. Simulation results show that the estimation of perceptual redundancy is helpful to the design of the watermarking scheme for colour images. The performance in terms of robustness and transparency of the proposed watermarking scheme is superior to that of the existing scheme.     

Multisensor image registration via implicit similarity

This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image. No explicit similarity measure that uses the intensities of both images is used. The computation utilizes the full spatial information of the first image and the accuracy and robustness of the registration depend only on it. False matchings are detected and adaptively weighted using a directional similarity measure. By embedding the scheme in a "coarse to fine" formulation, we were able to estimate affine and projective global motions, even when the images were characterized by complex space varying intensity transformations. The scheme is especially suitable when one of the images is of considerably better quality than the other (noise, blur, etc.). We demonstrate these properties via experiments on real multisensor image sets.     

基于梯度投影的图像盲复原算法

为了利用多帧退化图像信息快速恢复出高质量的图像,提出了一种新的递归梯度投影多帧图像盲复原算法。该算法充分利用多帧图像的先验信息,首先给出一种能够有效抑制噪声放大的新的代价函数,然后通过梯度投影算法对新的代价函数进行最小化以推导出迭代公式,最后通过频率域多次递归迭代运算对退化图像进行复原。模拟实验结果证明该算法运算快速,对于不同高斯噪声级别和不同PSF衰退的图像,均能够清晰地恢复出图像细节特征、同时也能够准确地恢复出衰退PSF。     

Gaussian mixture model learning based image denoising method with adaptive regularization parameters

Gaussian mixture model learning based image denoising as a kind of structured sparse representation method has received much attention in recent years. In this paper, for further enhancing the denoised performance, we attempt to incorporate the gradient fidelity term with the Gaussian mixture model learning based image denoising method to preserve more fine structures of images. Moreover, we construct an adaptive regularization parameter selection scheme by combing the image gradient with the local entropy of the image. Experiment results show that our proposed method performs an improvement both in visual effects and peak signal to noise values.     

Various dimensional colour image encryption based on non-overlapping block-level diffusion operation

This paper proposes a colour image encryption scheme to encrypt colour images of arbitrary sizes. In this scheme, a fixed block size (3 × 8) based block-level diffusion operation is performed to encrypt arbitrary sized images. The proposed technique overcomes the limitation of performing block-level diffusion operations in arbitrary sized images. This method first performs bit-plane decomposition and concatenation operation on the three components (blue, green, and red) of the colour image. Second it performs row and column shuffling operation using the Logistic-Sine System. Then the proposed scheme executes block division and fixed block-level diffusion (exclusive-OR) operation using the key image generated by the Piece-wise Linear Chaotic Map. At last, the cipher image is generated by combining the diffused blocks. In addition, the SHA-256 hashing on plain image is used to make chaotic sequences unique in each encryption process and to protect the ciphertext against the known-plaintext attack and the chosen-plaintext attack. Simulation results and various parameter analysis demonstrate the algorithm’s best performance in image encryption and various common attacks.

     

基于统计处理和B样条小波变换的毫米波图像边缘检测

针对毫米波图像噪声强、分辨率低的特点,提出了一种新的边缘检测方法。该方法首先根据统计信号处理理论定义了标准化梯度强度这一物理量;然后采用3次B样条函数的一阶导数作为边缘检测算子,由小波变换后得到的图像水平、垂直方向的高频信息,并根据这些信息确定出标准化梯度强度;接着采用单门限的处理得到图像粗边缘;最后对粗边缘施行非最大抑制处理和滤波来得到检测结果。实验证明,该方法可用固定的单门限自动、快速地检测出毫米波图像中人体背景下物体的边缘,满足安检需要。     

基于偏微分方程的误差分散逆半调改进算法

结合误差分散半调噪声特征,提出一种基于偏微分方程的逆半调改进算法。通过研究偏微分方程的去噪原理,以3次B样条函数作为扩散函数,采用迭代求解偏微分方程的方法估计逆半调图像,计算每次迭代前后图像梯度模值的增量以确定平滑度的调节参数,解决偏微分方程在应用中的参数选择自适应问题。实验结果表明,该算法在图像整体平滑度和细节保持能力上都具有较好的效果。     

A scheme of colour image retrieval from databases

This paper presents a scheme of image retrieval from a database using queries prompted by the colour and the shape of the objects present in different scenes. Of the whole scheme of image retrieval, we will focus attention on the modules that allow feature extraction of the component objects from the scenes and the matching of the objects among the different images. The defined scheme enables the indexing of images by measuring the similarity between the integral objects.     

A multi-direction GVF snake for the segmentation of skin cancer images

A multi-direction gradient vector flow (GVF) snake-based scheme is proposed in this paper for the segmentation of skin cancer images. Unlike the traditional anisotropic diffusion (AD) filter, which has many disadvantages such as sensitivity to noise, a new AD filter has been developed to remove the noise. The proposed AD filter uses adaptive threshold selection and a new gradient computation method, which is robust to noise and can effectively remove the hairs. After the noise is removed from the skin cancer image, the image is segmented using a multi-direction GVF snake. The multi-direction GVF snake extends the single direction GVF snake and traces the boundary of the skin cancer even if there are other objects near the skin cancer region. Experiments performed on 11 cancer images show the effectiveness of the proposed algorithm.     

A new watermarking scheme for colour image using QR decomposition and ternary coding

At present, the binary images are often used as the original watermark images of many watermarking methods, but partial methods cannot be easily extended to colour image watermarking methods. For resolving this problem, we propose a new watermarking method using ternary coding and QR decomposition for colour image. In the procedure of embedding watermark, the colour image watermark is coded to ternary information; the colour host image is also separated into image blocks of sized 3?×?3, and these image blocks are further decomposed via QR decomposition; then, one ternary watermark is embedded into one orthogonal matrix Q of QR decomposition by the proposed rules. In the procedure of extracting watermark, the proposed method uses the blind-manner to extract the embedded ternary information. The novelty of this scheme lies in the proposed ternary coding for watermark image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the watermarking scheme. The results of simulation show the presented technique is better than other compared schemes with respect to imperceptibility, embedded watermark capacity and real-time feature under the similar robustness.

     

自适应梯度幅值和形态学组合滤波算法

针对传统梯度幅值滤波算法中阈值需人工确定而无法正确判断噪声点和产生新噪声点的问题,提出了一种自适应梯度幅值和多结构元素形态学组合滤波算法。首先使用梯度幅度均值与方差的和作为阈值,以及Otsu算法自适应阈值两种自适应滤波算法对图像进行滤波,以自动确定滤波阈值,最大限度滤除噪声点,保持图像细节;然后使用多结构元素组成串联形态学复合滤波器对自适应梯度幅值滤波后的图像进一步滤波,以解决在自适应滤波过程中产生的新噪声问题。通过实验结果对比与分析,该算法滤波后的图像视觉效果明显好于传统滤波方法,从而验证了该算法既能保持图像细节,又能实现较好的图像滤波效果。     

Multispectral image segmentation by a multichannel watershed‐based approach

Watershed transformation in mathematical morphology is a powerful morphological tool for image segmentation that is usually defined for greyscale images and applied to the gradient magnitude of an image. This paper presents an extension of the watershed algorithm for multispectral image segmentation. A vector‐based morphological approach is proposed to compute gradient magnitude from multispectral imagery, which is then input into watershed transformation for image segmentation. The gradient magnitude is obtained at multiple scales. After an automatic elimination of local irrelevant minima, a watershed transformation is applied to segment the image. The segmentation results were evaluated and compared with other multispectral image segmentation methods, in terms of visual inspection, and object‐based image classification using high resolution multispectral images. The experimental results indicate that the proposed method can produce accurate segmentation results and higher classification accuracy, if the scales and contrast parameter are appropriately selected in the gradient computation and subsequent local minima elimination. The proposed method shows encouraging results and can be used for segmentation of high resolution multispectral imagery and object based classification.     

Weight assignment for adaptive image restoration by neural networks

This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way.     

基于分形压缩编码的数字水印技术

提出一种利用分形编码方案在图像中嵌入数字水印的方法。对数字图像分别进行传统分形编码和频域分形编码后,通过码书的比较,选择一些R块,将这些块的编码方案更改, 即用次优匹配D块替代最优匹配D块,从而实现数字水印的嵌入。实验证明该方案对图像质量几乎没有影响,在抗模糊、噪声干扰和剪切方面有较好的鲁棒性。     

Gradient entropy metric and p-Laplace diffusion constraint-based algorithm for noisy multispectral image fusion

Noise is easily mistaken as useful features of input images, and therefore, significantly reducing image fusion quality. In this paper, we propose a novel gradient entropy metric and p-Laplace diffusion constraint-based method. Specifically, the method is based on the matrix of structure tensor to fuse the gradient information. To minimize the negative effects of noise on the selections of image features, the gradient entropy metric is proposed to construct the weight for each gradient of input images. Particularly, the local adaptive p-Laplace diffusion constraint is constructed to further suppress noise when rebuilding the fused image from the fused gradient field. Experimental results show that the proposed method effectively preserves edge detail features of multispectral images while suppressing noise, achieving an optimal visual effect and more comprehensive quantitative assessments compared to other existing methods.     

Authenticating corrupted photo images based on noise parameter estimation

Photo image authentication is an interesting and demanding field in the computer vision and image processing community. This research is motivated by its wide range of applications, which include smart card authentication systems, biometric passport systems, etc. In this paper, we propose a method of authenticating corrupted photo images based on noise parameter estimation. The proposed method first generates corrupted images by adjusting the noise parameters in the initial training phase. This set of corrupted images and the noise parameters can be represented by a linear combination of the prototypes of the corrupted images and the noise parameters. In the testing phase, the noise parameters of the corrupted photo image can be estimated with a corrupted image and an original image. Finally, we can make a synthesized photo image from the original photo image using the estimated noise parameters and verify it with the corrupted photo image. The experimental results show that the proposed method can estimate the noise parameters accurately and improve the performance of photo image authentication.     

Rician noise removal from MR images using novel adapted selective non-local means filter

The reduction of rician noise from MR images without degradation of the underlying image features has attracted much attention and has a strong potential in several application domains including medical image processing. Interpretation of MR images is difficult due to their tendency to gain rician noise during acquisition. In this work, we proposed a novel selective non-local means algorithm for noise suppression of MR images while preserving the image features as much as possible. We have used morphological gradient operators that separate the image high frequency areas from smooth areas. Later, we have applied novel selective NLM filter with optimal parameter values for different frequency regions of image to remove the noise. A method of selective weight matrix is also proposed to preserve the image features against smoothing. The results of experimentation performed using proposed adapted selective filter prove the soundness of the method. We compared results with the results of many well known techniques presented in literature like NLM with optimized parameters, wavelet based de-noising and anisotropic diffusion filter and discussed the improvements achieved.     

视觉显著性驱动的全景渲染图非局部降噪

目的 传统降噪方法通常忽视人眼感知因素,对不同区域的图像块都进行同等处理。当使用传统降噪算法对全景画面滤波处理时,全景画面两极区域容易产生模糊问题,尤其是通过视口观察时,该问题更加明显。针对此问题,提出一种视觉显著性驱动的蒙特卡洛渲染生成全景图非局部均值（visual saliency driven non-local means,VSD-NLM）滤波降噪算法。方法 在VSD-NLM算法中首先使用全景图显著区域检测算法获取全景画面的显著区域;然后使用梯度幅值相似性偏差辅助的非局部均值（gradient magnitude similarity deviation assisted non-local means,GMSDA-NLM）滤波算法,降低显著区域的噪声;同时设计并行非局部均值（parallel non-local means,P-NLM）滤波算法,加快降噪处理速度,降低非显著区域噪声;最后利用改进的Canny算法提取梯度特征,同时结合各向异性扩散引导滤波来优化降噪结果。结果 采用结构相似度（structural similarity,SSIM）和FLIP作为评价指标,来对比VSDNLM算法与非局部均值滤波算法、多特征非局部均值滤波算法以及渐进式去噪算法等其他算法的性能。实验结果表明,VSD-NLM算法的降噪结果在客观评价指标上均优于对比算法,SSIM值比其他算法平均提高14.7%,FLIP值比其他算法平均降低15.2%。在视觉效果方面,VSD-NLM算法能够减轻全景画面模糊,提升视觉感知质量。本文对GMSDA-NLM和P-NLM算法的有效性进行了实验验证,相较于非局部均值滤波算法,GMSDA-NLM算法能够有效去除噪声并保持图像细节的完整性。P-NLM算法在运行速度方面相较对比算法平均提高约6倍,与串行算法生成的图像之间的SSIM值可达到0.996。结论 本文算法能够更好地用于全景图降噪,滤波效果更佳,对全景电影制作应用有重要的理论和实际意义。     

一种基于极值和方向的滤波算法

提出了一种有效的椒盐噪声图像滤波算法。该算法首先对含有噪声的图像取3×3邻域,判断某点是否为邻域极值将全部像素点分为可疑噪声与信号点集合;其次对每一个可疑噪声点在其3×3邻域内构造八个方向的梯度算子模板,通过比较8个方向梯度的大小,进一步确定其是否为噪声点;最后对噪声点进行邻域的中值滤波。实验结果表明该算法在滤除噪声的同时,很好地保存了图像的原始信息且有较好的信噪比。     

An edge detection scheme based on least squares support vector machine in a contourlet HMT domain

In this paper, we have presented a new and effective edge detection scheme based on least squares support vector machine (LS-SVM) classification in a contourlet Hidden Markov Tree Model (contourlet HMT). First, the input noisy image is decomposed into coarser and finer coefficients using a contourlet HMT transform to derive an efficient multiscale and multidirectional image representation. Second, the feature vector is performed through spatial regularity in a contourlet HMT domain, and the coarser coefficients classified using LS-SVM classifier into two classes: noise coefficients and edge coefficients. Next, all noisy contourlet HMT coefficients are well denoised by the BayesShrink method.Finally, the denoised coefficients and edge coefficients are fused using the weighted average rule, and the inverse contourlet HMT is applied to obtain the edge image.Experimental results demonstrate that our scheme can attain improved performance over state-of-the-art edge detection approaches, both qualitatively and quantitatively. Tests were performed on several images from the Berkeley dataset corrupted with Gaussian noise and on other images such as a cameraman, pepper and medical images. The results illustrate that the performance of the proposed scheme is stable.