Adaptive regularized image interpolation using a probabilistic gradient measure

An adaptive image interpolation approach is proposed in this paper. The proposed approach imposes a regularization on the reconstructed high-resolution image to suppress the noise and blurring incurred in the observed low-resolution image. Furthermore, the proposed regularization scheme is steered by the local gradient information of the image, which is evaluated using a probabilistic measure. Experiments are conducted to demonstrate the superior performance of the proposed approach.     

Saliency adaptive super-resolution image reconstruction

A saliency-based approach is proposed in this paper to perform super-resolution image reconstruction, with the aim to produce a higher-resolution image based on its low-resolution counterparts. The proposed approach adaptively adjusts the degree of regularization using the saliency measure of the local content of the image. Furthermore, a gradient-based criterion is proposed to measure the saliency of the image. Experiments are conducted to demonstrate the superior performance of the proposed approach.     

An optimal robust digital image watermarking based on SVD using differential evolution algorithm

The main objective in developing a robust image watermarking technique is to obtain the highest possible robustness without losing the transparency. To achieve this objective, this paper presents a new optimal robust image watermarking technique based on singular value decomposition (SVD) using differential evolution algorithm (DE). The singular values (SV) of the host image are modified by multiple scaling factors to embed a watermark image. The modifications are optimised using DE to achieve maximum robustness and transparency Experimental results show that the proposed approach can effectively improve the quality of the watermarked image and the robustness of the embedded watermark against various attacks.     

Sparse MRI reconstruction using multi-contrast image guided graph representation

Accelerating the imaging speed without sacrificing image structures plays an important role in magnetic resonance imaging. Under-sampling the k-space data and reconstructing the image with sparsity constraint is one efficient way to reduce the data acquisition time. However, achieving high acceleration factor is challenging since image structures may be lost or blurred when the acquired information is not sufficient. Therefore, incorporating extra knowledge to improve image reconstruction is expected for highly accelerated imaging. Fortunately, multi-contrast images in the same region of interest are usually acquired in magnetic resonance imaging protocols. In this work, we propose a new approach to reconstruct magnetic resonance images by learning the prior knowledge from these multi-contrast images with graph-based wavelet representations. We further formulate the reconstruction as a bi-level optimization problem to allow misalignment between these images. Experiments on realistic imaging datasets demonstrate that the proposed approach improves the image reconstruction significantly and is practical for real world application since patients are unnecessarily to stay still during successive reference image scans.     

Adaptive estimation of depth map for two-dimensional to three-dimensional stereoscopic conversion

With increasing demands of three-dimensional (3D) contents, the conversion of many two-dimensional (2D) contents to 3D contents has become a focus in 3D image processing. The most important and difficult problem in 2D-to-3D conversion is the estimation of the depth map using only a single-view image. Therefore, a novel method for estimating a depth map for stereoscopic conversion is proposed in this paper. A simulated haze image generated by adding a haze veil on the input image is used to represent salient region segmentation. The 3D stereoscopic image is generated on the basis of the depth map estimated by the haze removal algorithm without any heuristic cues or user interaction. A comparative study and quantitative evaluation with some other state of the art conversion methods are carried out, which demonstrate that using the proposed approach, similar or better-quality results may be obtained.     

基于HVS的小波域信息隐藏方法

为了提高信息隐藏的不可感知性和鲁棒性,结合人眼视觉系统(HVS)的相关知识,提出了一种新的小波域信息隐藏算法。将载体图像的中等亮度区域设为不可用区域,不在该区域隐藏信息,同时选择在载体图像小波变换的高频系数细节子图隐藏信息,并优先选择在对角高频系数细节子图中隐藏。计算局部子块的方差值作为隐藏信息的嵌入系数,并通过计算高频子图的噪声敏感模型ωθ(i,j)决定隐藏信息的位置。充分考虑了人眼的各种视觉掩蔽效应,使得隐藏信息后的结果图像相比隐藏之前没有出现明显的降质。实验结果表明该方法能够有效提高信息隐藏的不可知性,达到了很好的隐藏效果,同时能够有效地抗击噪声、裁剪和压缩等攻击影响,具有很好的鲁棒性,在信息安全领域有较好的应用前景。     

Directional variation adaptive image resolution enhancement

The goal of image interpolation is to produce a high-resolution image from its low-resolution counterpart. It has significant applications in video sensor network, where the resolution of images usually needs to be enhanced at the end user due to the limited transmission bandwidth. The key challenge of image interpolation is to preserve the edge structure of the image. In this paper, a new image interpolation approach is proposed to adaptively adjust the interpolation according to the directional variations of images. More specifically, at each pixel position to be interpolated, its neighboring pixels are projected onto 1D direction according to a number of proposed patterns. Then the direction, of which the variation is smallest, is chosen as the direction to perform image interpolation. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms.     

基于二进制小波变换和改进SPIHT算法的图像编码方法

提出了一种基于二进制小波变换和改进SPIHT算法的图像编码方法.二进制小波变换将图像从实数域变换到实数域,消除像素之间的空间冗余性,得到了具有整数准确度的紧致描述.针对传统SPIHT算法解码图像视觉效果差的缺点,提出了改进方法.根据图像分析结果,将二进制小波变换变换系数按视觉重要性重新排序,通过对视觉重要系数优先编码,把量化误差集中在视觉不敏感区域,从而在不影响编码率失真性能的同时,有效地提高了解码图像的视觉效果.实验结果表明,和其它流行的编码算法相比,本文算法对不同性质的图像具有最优的编码性能和视觉效果.     

A robust SVD-based image watermarking using a multi-objective particle swarm optimization

The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub-bands, singular value decomposition is applied to a chosen detail sub-band. Then, the singular values of the chosen sub-band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi-objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.     

Edge adaptive image resolution enhancement in video sensor network

Video sensor network usually uses fairly low-resolution images due to the limited transmission bandwidth in transmitting images. It is potential to enhance the captured low-resolution images using image resolution enhancement technique that is able to produce a high-resolution image from its low-resolution counterpart. The key challenge of image resolution enhancement is to preserve the edge structure in images. In this paper, a new image resolution enhancement approach is proposed to estimate the intensity of the unknown pixel using a bilateral weighted average of that of its neighboring pixels. More specifically, the neighboring pixels with nearer distance have larger contributions. Furthermore, the neighboring pixels belonging to direction with smaller variation have larger contributions. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms. Furthermore, the proposed approach yields low computational complexity; it is potential for real-time implementation.     

基于模糊判决的图像边缘连接

边缘连接是图像边缘提取中的一项重要的后处理技术。基于模糊判决理论 ,在由Sobel算法得到的边缘图像的基础上 ,给出了一套行之有效的边缘连接技术。结果表明 ,用所提出的算法提取目标图像的边缘时 ,能够在保持原有图像不失真的情况下 ,较大地提高边缘图像边界的连通性 ,为光电跟踪器和成像雷达的目标检测、跟踪和识别等提供了良好的基础。因而 ,本算法具有较高的实际应用价值     

压缩感知在合成发射孔径医学超声成像中的应用

为了解决合成发射孔径技术在医学超声成像实现中面临的数据量大及接收通道多的问题,提出一种超声成像系统频率域稀疏性模型的压缩感知成像算法。首先对超声系统频率域稀疏性模型进行了验证;然后根据稀疏性模型利用压缩感知理论对回波信号进行压缩采样,并使用最优化方法完成回波信号重建;最终通过合成发射孔径技术完成超声成像。针对医学成像中常用的点目标及模拟胎儿目标进行成像仿真实验,对重建图像在均方误差、分辨率及成像质量等方面与常规成像结果对比分析。实验结果表明在保证成像质量的同时,仅使用30%原始数据量及50%总接收通道数目可完成成像;频率域稀疏性模型的压缩感知成像算法可以大幅度减少合成发射孔径成像所需数据量及接收通道数,极大地降低了系统复杂度。      

High-performance rapid MR parameter mapping using model-based deep adversarial learning

PurposeTo develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.MethodsThe proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T₂ mapping of the brain and the knee at an acceleration rate R = 8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate the reconstruction performance of the proposed method.ResultsThe proposed adversarial learning approach achieved accurate T₂ mapping up to R = 8 in brain and knee joint image datasets. Compared to conventional reconstruction approaches that exploit image sparsity and low-rankness, the proposed method yielded lower errors and higher similarity to the reference and better image sharpness in the T₂ estimation. The quantitative metrics were normalized root mean square error of 3.6% for brain and 7.3% for knee, structural similarity index of 85.1% for brain and 83.2% for knee, and tenengrad measures of 9.2% for brain and 10.1% for the knee. The adversarial approach also achieved better performance for maintaining greater image texture and sharpness in comparison to the CNN approach without adversarial learning.ConclusionThe proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.     

A new approach for colored watermarking image into gray scale image using wavelet fusion

With the quick growth of technologies and great spread of the Internet, many challenges face data hiding. Nevertheless, significant data may be protected by burring it in an image as a watermark. This paper shows an efficient data hiding watermarking approach for color image by using singular value decomposition (SVD), multi-level discrete wavelet transform (DWT) and wavelet fusion. The main idea in this work is to separate the color image into its basic components (three channels); red, green, and blue. Then, fuse every channel with an image which is gray scale and integrate the three fused images into one gray scale fused image. Finally, the fused image is burred into a cover gray scale image to produce the watermarked image by using DWT and SVD. The proposed approach evaluation is done by using several images and different hacking on the transmitted image. The experimental results show that the marked images which are generated by the proposed approach are tolerant to versatile attacks such as Gaussian, blur, wrap, and cropping. Above all, the extracted watermark images are recognized even when the watermarked images suffered from attacks.     

Image matching using Gradient Orientation Selective Cross Correlation

Image matching has been an important topic in computer vision and image processing. A new approach named Gradient Orientation Selective Cross Correlation is proposed for image matching. In the new approach, a gradient orientation selectivity strategy is proposed to exclude the wrong points from correlation, especially for partial occlusion and some other ill-conditions. Experimental results are shown to demonstrate robustness, efficiency, and accuracy of the algorithm.     

Blind motion deblurring using multiple images

Recovery of degraded images due to motion blurring is a challenging problem in digital imaging. Most existing techniques on blind deblurring are not capable of removing complex motion blurring from the blurred images of complex structures. One promising approach is to recover the clear image using multiple images captured for the scene. However, in practice it is observed that such a multi-frame approach can recover a high-quality clear image of the scene only after multiple blurred image frames are accurately aligned during pre-processing, which is a very challenging task even with user interactions. In this paper, by exploring the sparsity of the motion blur kernel and the clear image under certain domains, we propose an alternative iteration approach to simultaneously identify the blur kernels of given blurred images and restore a clear image. Our proposed approach is not only robust to image formation noises, but is also robust to the alignment errors among multiple images. A modified version of linearized Bregman iteration is then developed to efficiently solve the resulting minimization problem. Experiments show that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with minimal requirements on the accuracy of image alignment. As a result, our method is capable of automatically recovering a high-quality clear image from multiple blurred images.     

红外和彩色可见光图像亮度-对比度传递融合算法

以红外和彩色可见光图像为研究对象,提出了一种基于亮度-对比度传递（LCT）技术的彩色图像融合算法。首先借助灰度融合方法将红外图像与彩色可见光图像亮度分量融合,然后用LCT技术改善灰度融合结果的亮度和对比度,最后利用快速YCBCR变换融合策略在RGB空间内直接生成彩色融合图像。文中利用像素平均融合法和多分辨率融合法作为不同的灰度融合措施以分别满足高实时性和高融合质量的需求。实验结果表明,提出算法的融合结果不仅具有与输入彩色可见光图像相近的自然色彩,而且具备令人满意的亮度和对比度,即使采用运算简单的像素平均法进行灰度融合,同样可以获得良好的融合效果。     

基于微位移技术提高CCD分辨率的方法

利用微小相对移动和计算机合成技术提高CCD等数字图像传感器分辨率是一个行之有效的方法.本文在这一技术的基础上,将CCD图像传感器按像元尺寸和不感光间隔尺寸二者之间的关系做出了分类,提出了适应更多图像传感器类型的高分辨率图像的获取方法和算法,并给出了某些类型的CCD图像传感器通过微位移方式提高分辨率的限度.计算机模拟和实验都验证了这种新方法的可行性.     

三维可逆混沌映射的图像加密算法

提出了一种三维可逆混沌映射图像加密算法。基于Line map二维混沌可逆映射,推导了该三维可逆映射的数学表达式。将灰度图像用一个三维矩阵数据描述,并按照所提出的算法将其组成一个二维的二进制图像。首先对此图像应用Line map二维混沌可逆映射进行像素置乱处理,然后再将置乱后的二进制图像还原成十进制的灰度图像,这样就得到了加密后的图像。所提出的方法可以通过一次三维可逆混沌映射同时实现图像加密的两个步骤,即像素置乱和像素混淆。仿真实验结果表明了该算法的有效性,且加密速度快、安全性高、简单易行。     

Magnetic resonance image enhancement using stochastic resonance in Fourier domain

Objective

In general, low-field MRI scanners such as the 0.5- and 1-T ones produce images that are poor in quality. The motivation of this study was to lessen the noise and enhance the signal such that the image quality is improved. Here, we propose a new approach using stochastic resonance (SR)-based transform in Fourier space for the enhancement of magnetic resonance images of brain lesions, by utilizing an optimized level of Gaussian fluctuation that maximizes signal-to-noise ratio (SNR).

Materials and Methods

We acquired the T1-weighted MR image of the brain in DICOM format. We processed the original MR image using the proposed SR procedure. We then tested our approach on about 60 patients of different age groups with different lesions, such as arteriovenous malformation, benign lesion and malignant tumor, and illustrated the image enhancement by using just-noticeable difference visually as well as by utilizing the relative enhancement factor quantitatively.

Results

Our method can restore the original image from noisy image and optimally enhance the edges or boundaries of the tissues, clarify indistinct structural brain lesions without producing ringing artifacts, as well as delineate the edematous area, active tumor zone, lesion heterogeneity or morphology, and vascular abnormality. The proposed technique improves the enhancement factor better than the conventional techniques like the Wiener- and wavelet-based procedures.

Conclusions

The proposed method can readily enhance the image fusing a unique constructive interaction of noise and signal, and enables improved diagnosis over conventional methods. The approach well illustrates the novel potential of using a small amount of Gaussian noise to improve the image quality.