Perceptual-based image fusion for hyperspectral data

Three hierarchical multiresolution image fusion techniques are implemented and tested using image data from the Airborne Visual/Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The methods presented focus on combining multiple images from the AVIRIS sensor into a smaller subset of images white maintaining the visual information necessary for human analysis. Two of the techniques are published algorithms that were originally designed to combine images from multiple sensors, but are shown to work well on multiple images from the same sensor. The third method presented was developed specifically to fuse hyperspectral images for visual analysis. This new method uses the spatial frequency response (contrast sensitivity) of the human visual system to determine which features in the input images need to be preserved in the composite image(s) thus ensuring the composite image maintains the visually relevant features from each input image. The image fusion algorithms are analyzed using test images with known image characteristics and image data from the AVIRIS hyperspectral sensor. After analyzing the signal-to-noise ratios and visual aesthetics of the fused images, contrast sensitivity based fusion is shown to provide excellent fusion results and, in every case, outperformed the other two methods     

Multi-scale fusion residual encoder-decoder approach for low illumination image enhancement

The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise, which leads to a reduction in image quality. A multi-scale fusion residual encoder-decoder (FRED) was proposed to solve the problem. By directly learning the end-to-end mapping between light and dark images, FRED can enhance the image's brightness with the details and colors of the original image fully restored. A residual block (RB) was added to the network structure to increase feature diversity and speed up network training. Moreover, the addition of a dense context feature aggregation module (DCFAM) made up for the deficiency of spatial information in the deep network by aggregating the context's global multi-scale features. The experimental results show that the FRED is superior to most other algorithms in visual effect and quantitative evaluation of peak signa-to-noise ratio (PSNR) and structural similarity index measure (SSIM). For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively, a satisfactory visual quality is obtained under the enhancement of low-light.     

A modified statistical approach for image fusion using wavelet transform

The fusion of images is an important technique within many disparate fields such as remote sensing, robotics and medical applications. For image fusion, selecting the required region from input images is a vital task. Recently, wavelet-based fusion techniques have been effectively used to integrate the perceptually important information generated by different imaging systems about the same scene. In this paper, a modified wavelet-based region level fusion algorithm for multi-spectral and multi-focus images is discussed. Here, the low frequency sub-bands are combined, not averaged, based on the edge information present in the high frequency sub-bands, so that the blur in fused image can be eliminated. The absolute mean and standard deviation of each image patch over 3 × 3 window in the high-frequency sub-bands are computed as activity measurement and are used to integrate the approximation band. The performance of the proposed algorithm is evaluated using the entropy, fusion symmetry and peak signal-to-noise ratio and is compared with recently published results. The experimental result proves that the proposed algorithm performs better in many applications.     

A novel approach using structure tensor for medical image fusion

Medical image fusion has been shown to be effective in supporting clinicians make better clinical diagnoses. Although many algorithms have been proposed for synthesis, they still have certain limitations. Some limitations can be seen as the synthesized image is reduced in contrast or details are not preserved. In this paper, we propose an image fusion algorithm to solve the problems mentioned above. Firstly, an image decomposition method is proposed to decompose the image into two components. This method is based on the Gaussian filter and the Weighted mean curvature filter. Secondly, a fusion method for high-frequency components is based on local energy function using Structure tensor saliency. Finally, we create an adaptive fusion rule using the Marine Predators Algorithm optimization method to fuse low-frequency components. Five latest algorithms and five evaluation indexes have been used to test the proposed algorithm’s effectiveness. The obtained experimental results show that the composite image is significantly improved in quality as well as well preserved the information from the input image.

     

Artery-vein separation via MRA--an image processing approach

This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object delineation principles and algorithms. The first step of this separation process is the segmentation of the entire vessel structure from the background and other clutter via absolute fuzzy connectedness. The second step is to separate artery from vein within this entire vessel structure via iterative relative fuzzy connectedness. After seed voxels are specified inside artery and vein in the CE-MRA image, the small regions of the bigger aspects of artery and vein are separated in the initial iterations, and further detailed aspects of artery and vein are included in later iterations. At each iteration, the artery and vein compete among themselves to grab membership of each voxel in the vessel structure based on the relative strength of connectedness of the voxel in the artery and vein. This approach has been implemented in a software package for routine use in a clinical setting and tested on 133 CE-MRA studies of the pelvic region and two studies of the carotid system from six different hospitals. In all studies, unified parameter settings produced correct artery-vein separation. When compared with manual segmentation/separation, our algorithms were able to separate higher order branches, and therefore produced vastly more details in the segmented vascular structure. The total operator and computer time taken per study is on the average about 4.5 min. To date, this technique seems to be the only image processing approach that can be routinely applied for artery and vein separation.     

模糊逻辑及其在数据融合中的应用

自从１９６５年Ｚａｄｅｈ发表关于模糊集理论的文章以来,模糊集理论已在工业控制、医疗诊断、经济决策、模式识别等领域得到广泛应用,随着模糊逻辑和可能性理论的提出和深入研究,它们在不确定推理模型的设计和多传感器信息融合中显示出越来越强大的优势,文中探讨它们在多传感器数据融合中的应用。     

数据链网络信息融合的有中心无中心处理结构

针对数据链网络的发展趋势及应用需求,揭示了有中心无中心融合处理的基本原理,并对其处理流程与关键技术进行了深入分析,并提供了解决思路及仿真分析。在此基础上,结合机载信息融合的功能及应用,分析对比了有中心无中心融合处理结构的优缺点,并提出了适合当前数据链网络现状的融合处理方式建议,对网络化多平台信息系统的信息融合架构设计、方案选择具有重要参考意义。     

数据融合技术在图像边缘检测中的应用

提出了一种基于数据融合的图像边缘检测算法。此种方法对初始图像分别采用“Rob-erts”算子和“sobel”算子及小波变换三种方法进行边缘提取,再将三种算法结果通过数据融合的手段获得一幅新的边缘图像。实验结果表明,该方案所得到的边缘图像的效果优于单独采用“Sobel”或“Roberts”算子所获得的边缘图像,是一种行之有效的图像边缘提取算法。     

Real-time image processing approach to measure traffic queueparameters

The real-time measurement of various traffic parameters including queue parameters is required in many traffic situations such as accident and congestion monitoring and adjusting the timings of the traffic lights. In case of the queue detection, at least two algorithms have been proposed by previous researchers. Those algorithms are used for queue detection and are unable to measure queue parameters. The authors propose a method based on applying the combination of noise insensitive and simple algorithms on a number of sub-profiles (a one-pixel-wide key-region) along the road. The proposed queue detection algorithm consists of motion detection and vehicle detection operations, both based on extracting edges of the scene, to reduce the effects of variation of lighting conditions. To reduce the computation time, the motion detection operation continuously operates on all the sub-profiles, but the vehicle detection is only applied to the tail of the queue. The proposed algorithms have been implemented on an 80386-based microcomputer system and the whole system works in real-time     

一种用于增强拼接图像成像质量的数据处理系统

传统图像拼接技术往往由计算机完成图像数据的处理,但计算机系统存在尺寸大、成本高、便携性差的缺点,为此,设计一种基于图像增强算法的嵌入式系统。该系统由FPGA完成图像采集与增强处理,然后将优化后的两组图像传输给DSP,最终完成两组图像的拼接。设计图像增强算法,对拼接区域不同灰度条件下进行了分类处理,从而实现了对图像特征点的增强;设计基于特征点向量距离比较的匹配算法,从而优化了图像拼接效果。对比该系统的测试结果与传统算法的拼接效果可知,在灰度变化较小的图像区域中,两种方法的拼接结果基本一致;而在灰度变化较大的图像区域中,传统方法的平均处理造成了特征点区域弥散,从而产生一定的失真,相比之下,该系统在1,2,3特征区域拼接效果明显优于传统方法。     

Modeling human-like intelligent image processing: An information processing perspective and approach

We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about “What is information?” In particular, what is “visual information”—human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of “information”, which can be derived from Kolmogorov's complexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.     

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.     

Novel cooperative neural fusion algorithms for image restoration and image fusion.

To deal with the problem of restoring degraded images with non-Gaussian noise, this paper proposes a novel cooperative neural fusion regularization (CNFR) algorithm for image restoration. Compared with conventional regularization algorithms for image restoration, the proposed CNFR algorithm can relax need of the optimal regularization parameter to be estimated. Furthermore, to enhance the quality of restored images, this paper presents a cooperative neural fusion (CNF) algorithm for image fusion. Compared with existing signal-level image fusion algorithms, the proposed CNF algorithm can greatly reduce the loss of contrast information under blind Gaussian noise environments. The performance analysis shows that the proposed two neural fusion algorithms can converge globally to the robust and optimal image estimate. Simulation results confirm that in different noise environments, the proposed two neural fusion algorithms can obtain a better image estimate than several well known image restoration and image fusion methods.     

Parallel image processing with the block data parallel architecture

Many digital signal and image processing algorithms can be speeded up by executing them in parallel on multiple processors. The speed of parallel execution is limited by the need for communication and synchronization between processors. In this paper, we present a paradigm for parallel processing that we call the block data flow paradigm (BDFP). The goal of this paradigm is to reduce interprocessor communication and relax the synchronization requirements for such applications. We present the block data parallel architecture which implements this paradigm, and we present methods for mapping algorithms onto this architecture. We illustrate this methodology for several applications including two-dimensional (2-D) digital filters, the 2-D discrete cosine transform, QR decomposition of a matrix and Cholesky factorization of a matrix. We analyze the resulting system performance for these applications with regard to speedup and efficiency as the number of processors increases. Our results demonstrate that the block data parallel architecture is a flexible, high-performance solution for numerous digital signal and image processing algorithms     

Combined image signal processing for CMOS image sensors

An efficient image signal processing structure is proposed for CMOS image sensors to achieve low area and power consumption. In the proposed structure, the gamma correction block is moved to the front to merge several image signal processings into one block. An efficient compensation scheme is also proposed to reduce the errors caused by the moving of the nonlinear gamma correction. Experimental results show that the proposed structure reduces area and power consumption by 23.8 and 31.1%, respectively.     

Fuzzy stack filters-their definitions, fundamental properties, andapplication in image processing

A new fuzzy filter, called fuzzy stack filter (FSF), is proposed to extend the filtering capability of conventional stack filter (SF), which is based on the positive Boolean function (PBF) as its window operator. We fuzzify the onset and off-set of a given PBF to obtain two types of fuzzy PBFs. Then, we adopt the architecture of threshold decomposition to develop this new fuzzy filter with a fuzzy PBF as its window operator. Each fuzzy PBF is associated with a set of control parameters. Therefore, the original PBF can be estimated from above and below by two fuzzy PBFs with appropriate control parameters. Furthermore, we can apply the fuzzy modifiers to modify the fuzzy PBFs such that the PBFs can be completely estimated by the fuzzy PBFs. Hence, the stack filter is a special case of fuzzy stack filter. Since some control parameters are added in this new filter, the neural learning algorithms can be easily developed under the flexibility of the given control parameters. We first propose the fuzzy (m,n) rank-order filter to test our proposed learning algorithm. In this simple learning algorithm, we can remove the noise-corrupted images very well in contrast to the filtering behavior of rank-order filters. We believe that the results presented will lead to more fruitful research on more advanced and powerful learning algorithms dedicated to the appropriate applications.     

Parallel architectures for image processing

Image processing is often considered a good candidate for the application of parallel processing because of the large volumes of data and the complex algorithms commonly encountered. This paper presents a tutorial introduction to the field of parallel image processing. After introducing the classes of parallel processing a brief review of architectures for parallel image processing is presented. Software design for low-level image processing and parallelism in high-level image processing are discussed and an application of parallel processing to handwritten postcode recognition is described. The paper concludes with a look at future technology and market trends