基于局部梯度特征的自适应多结点样条图像插值

为了获得质量更好的插值图像,提出了一种新的C^2。连续的支撑区间为（-2,2）的三次多结点样条插值核函数．通过增加结点带来的自由度构造了多结点样条插值公式;分析了在适当的边界条件和约束下三次多结点样条插值的逼近阶;将一维多结点样条插值算法推广到二维,建立了用于图像数据的插值公式;如果忽视图像的局部特征,通常双三次多结点样条插值图像的边缘会有模糊的现象,为此。对多结点样条插值应用逆梯度,得到了自适应多结点样条插值算法;实验所得误差图像和实验所得图像的峰值信噪比也证实了用自适应多结点样条插值算法重建的图像具有更高的质量．     

Preserving Topology by a Digitization Process

The main task of digital image processing is to recognize properties of real objects based on their digital images. These images are obtained by some sampling device, like a CCD camera, and represented as finite sets of points that are assigned some value in a gray-level or color scale. Based on technical properties of sampling devices, these points are usually assumed to form a square grid and are modeled as finite subsets of Z². Therefore, a fundamental question in digital image processing is which features in the digital image correspond, under certain conditions, to properties of the underlying objects. In practical applications this question is mostly answered by visually judging the obtained digital images. In this paper we present a comprehensive answer to this question with respect to topological properties. In particular, we derive conditions relating properties of real objects to the grid size of the sampling device which guarantee that a real object and its digital image are topologically equivalent. These conditions also imply that two digital images of a given object are topologically equivalent. This means, for example, that shifting or rotating an object or the camera cannot lead to topologically different images, i.e., topological properties of obtained digital images are invariant under shifting and rotation.     

基于矩形角点几何变换的畸变图像校正

在以数码相机等数字设备拍摄图像时,所拍摄的图像经常会产生各种各样的变形,这种变形可能会导致识别软件中的后续处理失败,从而使图像无法被识别。为了使普通的识别软件能够对数码相机等拍摄的图像进行识别,有必要对其进行校正。针对大部分畸变图像是倾斜变形和透视变形,采用类似倾斜变形图像的几何校正算法,分两步实现图像的校正。实验表明,该方法对于倾斜变形和透视变形有良好的校正效果,以便于后续的图像处理。     

Steady-state image processing

This paper presents a new approach to the application mode of image processing operators, the so-called steady-state image processing. The approach reminds a steady-state genetic processing of images by considering each pixel of the image as an individual. So, some pixels are selected, processed and copied back into the image. This differs from the standard approach, where all image pixels are processed at once. The proposed approach offers many choices for variation, and allows for the assignment of dynamic measures to images. This will serve new families of soft computing methods as, e.g. immune-based algorithms, which need images as non-static objects in order to fulfill reasonable tasks. This paper also introduces some basic steady-state operators and exemplifies the analysis of an image by means of a small example. Also, it is shown how steady-state image processing can be applied in the context of texture segmentation. Steady-state image processing can be considered a way of processing images, which is deeply inspired by genetic algorithms.     

构造标定自动机实现数字图像的Freeman链编码标定

图像的Freeman链编码是对图像边界的描述,这种链编码给我们图形一些基本特征,正在被广泛地应用到图像处理和图像识别中。本文给出了二值图像区域的标定方法。对于八近邻的图像,分别建立了一组最小的完备图。利用图像标定的基本图,为二值图像边界的识别构造了一个自动机,自动机的输出就是Freeman链编码,为二值图像区域的标定提供了一个有效算法。     

A survey of thresholding techniques

In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978, 259–265) and Fu and Mu (Pattern Recognit. 13, 1981, 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.     

数字图像反取证技术综述

目的 数字图像的真实性问题备受人们关注,被动取证是解决该问题的有效途径。然而,如果伪造者在篡改图像的同时利用反取证技术对篡改的痕迹进行消除或伪造,那么已有的大量被动取证技术都将失效。回顾图像反取证技术的研究现状（包括兴起原因、实现原理、技术特点以及应用前景）,并根据已有文献总结反取证技术面临的主要挑战和机遇。方法 由于现有的被动取证技术大都基于遗留痕迹和固有特征的异同来辨识图像真伪,因此本文以不同的取证特征为线索来评述和比较反取证技术的原理和策略。结果 根据取证特征的不同,将反取证技术归纳为遗留痕迹隐藏、固有特征伪造和反取证检测等三类,并展示了当前各类反取证技术面临的难点和挑战。结论 对数字图像反取证技术进行总结和展望,并指出其算法未来在通用性、安全性、可靠性等方面将有待进一步的深入研究。     

A parallel solution for high resolution histological image analysis

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.     

Machine Learning-Based Pruning Technique for Low Power Approximate Computing

Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits. Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error. In this work, a novel machine learning (ML) -based pruning technique is introduced to design digital circuits. The machine-learning algorithm of the random forest decision tree is used to prune nodes selectively based on their input pattern. In addition, an error compensation value is added to the original output to reduce an error rate. Experimental results proved the efficiency of the proposed technique in terms of area, power and error rate. Compared to conventional pruning, proposed ML pruning achieves 32% and 26% of the area and delay reductions in 8*8 multiplier implementation. Low power image processing algorithms are essential in various applications like image compression and enhancement algorithms. For real-time evaluation, proposed ML optimized pruning is applied in discrete cosine transform (DCT). It is a basic element of image and video processing applications. Experimental results on benchmark images show that proposed pruning achieves a very good peak signal-to-noise ratio (PSNR) value with a considerable amount of energy savings compared to other methods.     

A knowledge-based approach for retrieving images by content

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed     

State of the art in passive digital image forgery detection: copy-move image forgery

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks.     

对数字图像处理中正交变换的研究

在数字图像处理中,正交变换因其独特的性质而广泛运用,它是信号处理中最重要的一类变换。介绍了正交变换的重要性质,讨论了在数字图像处理中采用正交变换的意义,重点研究了正交变换的标准基,并以DCT为例,利用MATLAB绘出了标准基图像。任意一副数字图像正交变换过后,得到的就是诸多标准基图像的权重,在变换域处理的就是这些标准基图像的权重。对数字图像处理中正交变换理论的学习和教学有很大帮助。     

Object-aware image thumbnailing using image classification and enhanced detection of ROI

Thumbnail images are used to display a large collection of photos in various digital devices. It aims for people to browse and search the image collection effectively. The provided thumbnail images are expressed in a much lower resolution compared to the resolution of the original image. Thus, it faces a significant problem of how to represent the content of a given image effectively in a tiny thumbnail image. Many image thumbnailing methods have been presented in literature for this purpose. However, the existing thumbnailing methods are designed to use a single method to all kinds of images, regardless of image contents. On the other hand, the proposed method employs two different thumbnail generation methods either of which is applied according to corresponding image context. To achieve this, we first classify images into two groups by detecting the object existence. Then, an ROI cropping method using a saliency map is presented for images with objects, in order to represent the important region of images in the thumbnail. Images without any interesting objects, such as landscape images, are considered to be resized by using a simple scaling method to maintain the whole image context. Experimental results show that the proposed method yields comparable performance on a variety of datasets.     

A survey of passive technology for digital image forensics

Over the past years, digital images have been widely used in the Internet and other applications. Whilst image processing techniques are developing at a rapid speed, tampering with digital images without leaving any obvious traces becomes easier and easier. This may give rise to some problems such as image authentication. A new passive technology for image forensics has evolved quickly during the last few years. Unlike the signature-based or watermark-based methods, the new technology does not need any signature generated or watermark embedded in advance. It assumes that different imaging devices or processing would introduce different inherent patterns into the output images. These underlying patterns are consistent in the original untampered images and would be altered after some kind of manipulations. Thus, they can be used as evidence for image source identification and alteration detection. In this paper, we will discuss this new forensics technology and give an overview of the prior literatures. Some concluding remarks are made about the state of the art and the challenges in this novel technology.     

Digital Image Forensics via Intrinsic Fingerprints

Digital imaging has experienced tremendous growth in recent decades, and digital camera images have been used in a growing number of applications. With such increasing popularity and the availability of low-cost image editing software, the integrity of digital image content can no longer be taken for granted. This paper introduces a new methodology for the forensic analysis of digital camera images. The proposed method is based on the observation that many processing operations, both inside and outside acquisition devices, leave distinct intrinsic traces on digital images, and these intrinsic fingerprints can be identified and employed to verify the integrity of digital data. The intrinsic fingerprints of the various in-camera processing operations can be estimated through a detailed imaging model and its component analysis. Further processing applied to the camera captured image is modelled as a manipulation filter, for which a blind deconvolution technique is applied to obtain a linear time-invariant approximation and to estimate the intrinsic fingerprints associated with these postcamera operations. The absence of camera-imposed fingerprints from a test image indicates that the test image is not a camera output and is possibly generated by other image production processes. Any change or inconsistencies among the estimated camera-imposed fingerprints, or the presence of new types of fingerprints suggest that the image has undergone some kind of processing after the initial capture, such as tampering or steganographic embedding. Through analysis and extensive experimental studies, this paper demonstrates the effectiveness of the proposed framework for nonintrusive digital image forensics.     

基于裂隙灯的外眼图像采集与处理系统

介绍基于裂隙灯的外眼图像采集与处理系统,系统能过改造传统裂隙灯上的普通相机摄像光路,用数码相机取代传统光学相机,以获得高清晰度数码图像。利用数字图像处理技术对外眼图像进行处理,为临床提供客观和定量化的诊断依据。实现患者信息与图像高效存储、交流与远程传输。     

MATLAB语言在数字图像处理中的应用

MATLAB语言是以复数矩阵作为基本编程单元的一种程序设计语言,它提供了各种矩阵的运算与操作,并有较强的数字图像处理功能。运用MATLAB语言中的图像处理工具箱对数字图像的重要变换—离散余弦变换进行了应用举例,实例说明MATLAB语言非常适合数字图像处理的仿真及算法研究,为数字图像处理与算法研究带来了方便并且效率极高。     

谷物害虫图像识别中数理统计特征的提取

本文阐述了利用计算机数字图像处理技术,对谷物害虫图像的一阶灰度值直方图和图像的目标区域,进行自动提取其一阶灰度值统计量和几何形状等数理统计特征的主要技术和方法。试验结果表明：该方法可以为谷物害虫的计算机自动模式识别(快速分类)提供稳定的特征参数值,有效地提高了识别率。     

All-in-focus imaging using average filter-based relative focus measure

Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.     

NEQR: a novel enhanced quantum representation of digital images

Quantum computation is becoming an important and effective tool to overcome the high real-time computational requirements of classical digital image processing. In this paper, based on analysis of existing quantum image representations, a novel enhanced quantum representation (NEQR) for digital images is proposed, which improves the latest flexible representation of quantum images (FRQI). The newly proposed quantum image representation uses the basis state of a qubit sequence to store the gray-scale value of each pixel in the image for the first time, instead of the probability amplitude of a qubit, as in FRQI. Because different basis states of qubit sequence are orthogonal, different gray scales in the NEQR quantum image can be distinguished. Performance comparisons with FRQI reveal that NEQR can achieve a quadratic speedup in quantum image preparation, increase the compression ratio of quantum images by approximately 1.5X, and retrieve digital images from quantum images accurately. Meanwhile, more quantum image operations related to gray-scale information in the image can be performed conveniently based on NEQR, for example partial color operations and statistical color operations. Therefore, the proposed NEQR quantum image model is more flexible and better suited for quantum image representation than other models in the literature.