QuickBird Image Fusion by a Multirresolution-Multidirectional Joint Image Representation

A new fusion methodology for Multiespectral and Pancromatic images, has been proposed. This methodology is based on a joint multiresolution-multidirectional representation of the source images. For that an unique directional filters bank of low computacional complexity has been used.This kind of image representation allows an appropriated selection of the information extracted from the source images, avoiding some of the limitations inherent to other multiresolution fusión methods. The final aim is to obtain fused images with a high spectral and spatial quality simultaneously. The source images corresponds to the captured by Quickbird satellite (panchromatic and multispectral). The high quality of the obtained results shows the potential of the joint multiresolution-multidirectional representation for images fusion.     

Compressing image-based relighting data using eigenanalysis and wavelets

In image-based relighting (IBL) a tremendous number of reference images are needed to synthesise a high-quality novel image. This collection of reference images is referred as an IBL data set. An effective compression method for IBL data makes the IBL technique more practical. Within an IBL data set, there is a strong correlation among different reference images. In conventional eigen-based image compression methods, the principal component analysis (PCA) process is used for exploiting the correlation within a single image. Such an approach is not suitable for handling IBL data. The authors present an eigenimage-based method for compressing IBL data. The method exploits the correlation among reference images. Since there is a huge number of images and pixel values, the cascade recursive least square (CRLS) network based PCA is used to extract eigenimages. Afterwards, the wavelet approach is used for compressing those eigenimages. Simulation results demonstrate that this approach is much superior to that of compressing each reference image with JPEG and JPEG2000.     

The Enriched Feature Enhancement Technique for Satellite Image Based on Transforms Using PCNN

The features of the satellite images can be improved by fusing or combining two images with complementary property. By fusing these two images the spatial property of the resultant image is improved. Satellite images are one of the agents that give the features of the earth’s surface. Processing these satellite images will provide more geographical information hidden in the images. This research paper have an detailed insight study of two types of the satellite images one is Panchromatic (PAN) and other Multispectral (MS). The PAN image with high spatial resolution and MS image with spectral resolution are fused to get better resultant output. For fusion process Nonsubsampled Contour let Transform is used to decompose the images into low and high frequency values. Pulse Coupled Neural Network is used to motivate the low frequency pixel and Morphological filter is applied to the edge detected image for finding the features in the images. This is an real time transformations which will give better results in SAR image processing, video processing, stereo based reconstruction of depth and width of the features present in the image.

     

Teleconferencing with dynamic medical images

Dynamic images, a sequence of static images displayed in rapid succession and perceived as a continuous motion by the human eye, are widely used in medicine. One of the primary objectives of telemedicine is the transmission of such images to a distant location to manage clinical problems remotely. A broad variety of methods is available to acquire, store, transmit and display these images. However, the context of the clinical problem determines which of these methods can be deployed in a telemedicine solution. This paper discusses the advantages and disadvantages of the different technologies and presents an example of a teleconferencing system for interventional cardiology. This system acquires cardiac angiography and intravascular ultrasound images and transmits them over an existing Internet connection to a distant location. It is specifically optimized for clinical conferencing, where time is limited for each case presentation during the conference, compared to the relatively long time available for the conference preparation. The system takes advantage of this characteristic by transmitting the images well in advance of the clinical conference and displaying them synchronously at both locations during the conference. This allows for the preservation of the original image quality     

The information content of MR images

The theoretical information content, defined by C.E. Shannon (1948), is proposed as an objective measure of MR (magnetic resonance) image quality. This measure takes into account the contrast-to-noise ratio (CNR), scan resolution, and field of view. It is used to derive an optimum in the tradeoff problem between image resolution and CNR, and as a criterion to assess the usefulness of high-resolution (512(2)) MR images. The result tells that for a given total acquisition time, an optimum value of the resolution can be found. This optimum is very broad. To apply Shannon's theory on information constant to MR images, a model for the spatial spectral power density of these images is required. Such a model has been derived from experimental observations of ordinary MR images, as well as from theoretical considerations.     

Modeling,classifying and annotating weakly annotated images using Bayesian network

In this paper, we propose a probabilistic graphical model to represent weakly annotated images. We consider an image as weakly annotated if the number of keywords defined for it is less than the maximum number defined in the ground truth. This model is used to classify images and automatically extend existing annotations to new images by taking into account semantic relations between keywords. The proposed method has been evaluated in visual-textual classification and automatic annotation of images. The visual-textual classification is performed by using both visual and textual information. The experimental results, obtained from a database of more than 30,000 images, show an improvement by 50.5% in terms of recognition rate against only visual information classification. Taking into account semantic relations between keywords improves the recognition rate by 10.5%. Moreover, the proposed model can be used to extend existing annotations to weakly annotated images, by computing distributions of missing keywords. Semantic relations improve the mean rate of good annotations by 6.9%. Finally, the proposed method is competitive with a state-of-art model.     

An Object-Based Method for Sharing Secret High-Dynamic-Range Images

With the popularity of the Internet, how to securely transmit and store multimedia data, and effectively access them has become an issue of great concern. High-dynamic-range (HDR) images are predicted to be a future trend and have already received considerable attention. In this study, an object-based method for sharing secret HDR images is proposed. This method comprised three steps. First, HDR images were divided into several objects and the coordinates of the objects and related information were recorded. Second, a method for sharing secret images was used to share the produced objects and recorded information, which were ranked according to importance. Third, a Lagrange interpolation method was used to reconstruct the various objects. The method can be used to maintain high information security and provide a convenient and effective image-restoration method to users. Experimental results confirmed the effectiveness of the method proposed in this study.     

Adaptive speckle reduction filter for log-compressed B-scan images

A good statistical model of speckle formation is useful for designing a good adaptive filter for speckle reduction In ultrasound B-scan images. Previously, statistical models have been used, but they failed to account for the log compression of the echo envelope employed by clinical ultrasound systems. Log-compression helps in reducing the dynamic range of the B-scan Images for display on a monitor as well as enhancing weak backscatters. In this article, statistics of log-compressed echo images, using the K-distribution statistical model for the echo envelope, are used to derive a parameter that can be used to quantify the extent of speckle formation. This speckle quantification can be used with an unsharp masking filter to adaptively reduce speckle. The effectiveness of the filter is demonstrated on images of contrast detail phantoms and on in-vivo abdominal images obtained by a clinical ultrasound system with log-compression.     

Ten years of art imaging research

This paper describes a decade of work on digital imaging for museums. From 1989 to 1992, the visual arts system for archiving and retrieval of images (VASARI) project produced a digital-imaging system that made color-calibrated images of up to 20000×20000 pixels directly from paintings. It used seven color-separation bands in the visible region, resulting in an average color error of around 1 ΔE*_ab unit. These images have since been used to monitor the condition of paintings, document paintings during conservation treatment, including predicting appearance after cleaning, reconstruct the original appearance of paintings in which pigments have faded, and assess whether paintings have been damaged during transportation, in estimations of the surface reflectance spectra and in the printing of high-quality reproductions. We have applied similar techniques to museum infrared and X-ray imaging. To manage the images produced by the VASARI system, an image-processing package has been developed that is tailored for very large colorimetric images. This package has since been used in several other projects, including a remote image viewer designed to provide internet access to high-resolution images. The paper explores these developments and gives details of the current generation of VASARI-derived systems, set in the context of the state of the art for museum imaging     

真三维显示在医学教育与仿真中的应用

研究了真三维显示技术在医学影像中的应用。使用CT、MRI、DSA原始医学影像作为数据源,对影像进行图像分割与三维重建,在此基础上进行真三维数据转换,运用计算机视觉技术开发互动教学系统。构建了基于真三维显示技术的医学教育与仿真系统,可呈现与器官组织空间结构一致的立体医学影像,并进行互动仿真操作。真三维显示技术将是医学教育与仿真的重要手段,可为医学教育提供最逼真的模拟实践平台。     

Medical image categorization and retrieval for PACS using the GMM-KL framework.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.     

基于象素划分的两值线图图象的细化

两值图象的细化是图象分析的重要环节。本文提出一种基于象素划分的两值线图图象的细化算法。文中设计了三组模板用于两值线图图象的细化，对于细化中产生的多余分枝线，通过划分象素集合，按文中给出的搜索次序图进行搜索，根据对搜索结果判别以去除之，从而得到满意的细化结果。文中详细说明细化过程，并给出实验结果。该方法已在实际工作中得到运用。     

Motion compensating interpolation considering covered and uncovered background

An improved algorithm for motion compensating interpolation of images in digital television sequences is presented. The algorithm is applicable to the interpolation of skipped images in the environment of a low bit rate codec. It is based on the assumption of pure translatorily moving objects. A low complex hierarchical blockmatching algorithm is used to estimate displacement vector fields, which are a close approximation of the true motion. The estimated displacement vector fields are evaluated in combination with the information of a change detector, in order to derive a segmentation of the images into moving objects, static background, covered, and uncovered background. Especially in the case of interpolating more than one image between every two transmitted images, it has to be taken into account that for each of these images an individual displacement vector field and an individual segmentation is required. This is achieved through a transformation of motion information based on the estimation of one displacement vector field for each pair of transmitted images, only. A spatio-temporal filter, controlled by the displacement and segmentation information, is used to interpolate the desired number of images. Computer simulations confirm, that the presented algorithm provides a remarkably improved rendition of motion, even if video sequences are strongly distorted with coding noise caused by a low bit rate coding process.     

Robust B-spline Snakes For Ultrasound Image Segmentation

Snake-based methods are commonly used to segment ultrasound images. However, their performance is generally limited because of the specific properties of this kind of images. This paper addresses the sensitivity of parametric active contours to speckle within ultrasound images. We propose a new B-spline snake model, founded on two original external energies specifically tailored for the segmentation of biomedical speckled images. First, the curve is attracted from a wide capture range with an expansion energy that facilitates the snake initialization. Then, it is accurately fitted on the region boundaries with an energy that allows precise positioning of the curve along edges in ultrasound images. A mutual inhibition function is designed to control the two energies. Results on real ultrasound images are presented and quantitatively compared to the boundaries manually outlined by experts. Our method improves the precision of heart cavities segmentation.     

Depth-aware blending of smoothed images for Bokeh effect generation

Bokeh effect is used in photography to capture images where the closer objects look sharp and everything else stays out-of-focus. Bokeh photos are generally captured using Single Lens Reflex cameras using shallow depth-of-field. Most of the modern smartphones can take bokeh images by leveraging dual rear cameras or a good auto-focus hardware. However, for smartphones with single-rear camera without a good auto-focus hardware, we have to rely on software to generate bokeh images. This kind of system is also useful to generate bokeh effect in already captured images. In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images. The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network. The model is trained through three phases to generate visually pleasing bokeh effect. The proposed approach is compared against a saliency detection based baseline and a number of approaches proposed in AIM 2019 Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order to understand different parts of the proposed algorithm. The network is lightweight and can process an HD image in 0.03 s. This approach ranked second in AIM 2019 Bokeh effect challenge-Perceptual Track.     

Motion detection and motion compensation for digital subtraction angiography image enhancement

For the enhancement of digital subtraction angiography (DSA) images, registration of mask and contrast image prior to subtraction is a pre-requisite. One of the main requirements of this task is that the region-of-interest used for the calculation of the registration parameters should contain the vascular structures of interest. This, however, is also one of the main problems in DSA because the contrasted vascular structures can be regarded as a distortion that makes the images to be compared dissimilar. In this paper we present a comparison between three frequently used similarity measures and histogram-based similarity measures. This reveals the advantages of the latter. The data-driven approach is especially suitable for registration of two images which are identical except for some structures visible in one but not in the other image. Based on an energy similarity measure, a motion vector field is obtained by template matching, which gives a set of homologous landmarks or control points in the mask and contrast image. A point-based registration is performed fitting the parameter of an appropriate transformation for patient motion correction. An affine and an elastic transformation are compared for an abdominal fluoroscopic scene.     

Image compression using wavelets and JPEG2000: a tutorial

The demand for higher and higher quality images transmitted quickly over the Internet has led to a strong need to develop better algorithms for the filtering and coding of such images. The introduction of the JPEG2000 compression standard has meant that for the first time the discrete wavelet transform (DWT) is to be used for the decomposition and reconstruction of images together with an efficient coding scheme. The use of wavelets implies the use of subband coding in which the image is iteratively decomposed into high- and low-frequency bands. Thus there is a need for filter pairs at both the analysis and synthesis stages. This paper aims in tutorial form to introduce the DWT, to illustrate its link with filters and filterbanks and to illustrate how it may be used as part of an image coding algorithm. It concludes with a look at the qualitative differences between images coded using JPEG2000 and those coded using the existing JPEG standard     

A multimodal registration algorithm of eye fundus images usingvessels detection and Hough transform

Image registration is a real challenge because physicians handle many images. Temporal registration is necessary in order to follow the various steps of a disease, whereas multimodal registration allows us to improve the identification of some lesions or to compare pieces of information gathered from different sources. This paper presents an algorithm for temporal and/or multimodal registration of retinal images based on point correspondence. As an example, the algorithm has been applied to the registration of fluorescein images (obtained after a fluorescein dye injection) with green images (green filter of a color image). The vascular tree is first detected in each type of images and bifurcation points are labeled with surrounding vessel orientations. An angle-based invariant is then computed in order to give a probability for two points to match. Then a Bayesian Hough transform is used to sort the transformations with their respective likelihoods. A precise affine estimate is finally computed for most likely transformations. The best transformation is chosen for registration.     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

Confidence maps and confidence intervals for near infrared images in breast cancer

This paper extends basic concepts of statistical hypothesis testing and confidence intervals to images generated by a new procedure for near infrared spectroscopic tomography being developed for use in breast cancer diagnosis. By estimating the covariance matrix of the pixels of an image from data used in the image reconstruction process, confidence maps for statistical tests on individual pixels and confidence intervals for entire images are displayed as an aid to research and clinical personnel interpreting possibly noisy images. The methods are applied to simulated and phantom-based images.