An overview on applications and capabilities in the Meteosat image processing system

Since the launch of its first pre-operational model, the Meteosat system has evolved as one of the most well-known Earth observation satellite projects. In particular through its imagery mission it has become an indispensable tool for a large user community. Both the quality of the images produced by Meteosat satellites and their processing onground have been continuously improved in recent years. This paper is a short review of the present capabilities and applications of the Meteosat image processing at the European Space Operations Centre (ESOC, Darmstadt). It starts with a brief overview on available hardware tools for real-time and offline processing of Meteosat images. Geometrical image restoration and radiometric quality control are the main tasks in this area. But in addition a number of dedicated (software) tools and test setups had to be developed for special testing of the satellite performance, for instance during the commissioning of a new spacecraft. Finally, a short summary on current studies and new developments related to the Meteosat imagery mission concludes this review.     

In situ diagnostic capabilities for beam position and beam intensity monitoring at SyLMAND

SyLMAND, the Synchrotron Laboratory for Micro and Nano Devices at the Canadian Light Source, consists of a dedicated X-ray lithography beamline on a bend magnet port, and process support laboratories in a cleanroom environment. The beamline comprises a double mirror system with flat, chromium-coated silicon mirrors operated at varying grazing angles of incidence for spectral adjustment by high energy cut-off. We present in this paper, the in situ diagnostic components inside the vacuum vessel upstream and downstream of the mirrors that allow for monitoring the incident beam and the reflected beam after first, second, and both mirrors. Four fly wire systems are used for beam position monitoring and intensity measurements. Additionally, four detector plates mounted on and moving with the mirror bodies are used to determine the position of the mirror surfaces with respect to the beam. First experimental results verify the capabilities of the system by showing good agreement between measured and calculated data.     

自适应中值滤波算法在图像处理中的应用

为了比较自适应中值滤波相对于传统中值滤波在滤除数字图像中噪声时的优缺点,文章通过Matlab实现了中值滤波和自适应中值滤波仿真,分析了两种算法的滤波效果,从而从主观图像和客观参数上反映了自适应中值滤波算法的优越性,得到了它比传统中值滤波算法具有较好的图像去噪和细节保护性能的结论。     

Generalization capabilities of subtle image pattern classifiers

The generalization capabilities, for learned subtle image pattern categories, of neural network and algorithmic classification techniques are described. Several neural network and algorithmic techniques have been applied to a set of feature vectors extracted from thermal infrared images which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders, particularly with soft tissue injuries such as whiplash in which there are few widely agreed upon diagnostic standards. The results of this research indicate that the backpropagation neural network produces the best classification results and provides significantly better generalization from a set of training patterns. Results of unsupervised classification of the data using clustering algorithms and the Adaptive Resonance Theory (ART2) neural network demonstrate the difficulties of learning and of generalization of patterns from such data     

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.     

In situ image segmentation using the convexity of illumination distribution of the light sources

When separating objects from a background in an image, we often meet difficulties to obtain the precise output due to the unclear edges of the objects as well as the poor or nonuniform illumination. In order to solve this problem, this paper presents an in situ segmentation method which takes advantages of the distribution feature of illumination of light sources, rather than analyzing the image pixels themselves. After analyzing the convexity of illumination distribution (CID) of point and linear light sources, the paper makes use of the CID features to find pixels belonging to the background. Then some background pixels are selected as control points to reconstruct the image background by means of B-spline; finally, by subtracting the reconstructed background from the original image, global thresholding can be employed to make the final segmentation. Quantitative evaluation experiments are made to test the performance of the method.     

Analysis of all-in-focus image processing in image restoration

We discuss a new deblurring problems in this paper. Focus measurements play a fundamental role in image processing techniques. Most traditional methods neglect spatial information in the frequency domain. Therefore, this study analyzed image data in the frequency domain to determine the value of spatial information. but instead misleading noise reduction results . We found that the local feature is not always a guide for noise reduction. This finding leads to a new method to measure the image edges in focus deblurring. We employed an all-in-focus measure in the frequency domain, based on the energy level of frequency components. We also used a multi-circle enhancement model to analyze this spatial information to provide a more accurate method for measuring images. We compared our results with those using other methods in similar studies. Findings demonstrate the effectiveness of our new method.     

Quantum image processing?

This paper presents a number of problems concerning the practical (real) implementation of the techniques known as quantum image processing. The most serious problem is the recovery of the outcomes after the quantum measurement, which will be demonstrated in this work that is equivalent to a noise measurement, and it is not considered in the literature on the subject. It is noteworthy that this is due to several factors: (1) a classical algorithm that uses Dirac’s notation and then it is coded in MATLAB does not constitute a quantum algorithm, (2) the literature emphasizes the internal representation of the image but says nothing about the classical-to-quantum and quantum-to-classical interfaces and how these are affected by decoherence, (3) the literature does not mention how to implement in a practical way (at the laboratory) these proposals internal representations, (4) given that quantum image processing works with generic qubits, this requires measurements in all axes of the Bloch sphere, logically, and (5) among others. In return, the technique known as quantum Boolean image processing is mentioned, which works with computational basis states (CBS), exclusively. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside image processing too.     

数字图片处理算法

数字图片在人们生活中越来越普遍,随时随地拍照即可,已经成为人们生活中很重要的一部分,同时对图片处理的需求也越来越多。如何更好地处理图片,已经成为更多相关研究的重点。总结了一些常见的图片处理算法及相关图片处理软件,方便大家借鉴及应用。     

Space variant image processing

This paper describes a graph-based approach to image processing, intended for use with images obtained from sensors having space variant sampling grids. The connectivity graph (CG) is presented as a fundamental framework for posing image operations in any kind of space variant sensor. Partially motivated by the observation that human vision is strongly space variant, a number of research groups have been experimenting with space variant sensors. Such systems cover wide solid angles yet maintain high acuity in their central regions. Implementation of space variant systems pose at least two outstanding problems. First, such a system must be active, in order to utilize its high acuity region; second, there are significant image processing problems introduced by the non-uniform pixel size, shape and connectivity. Familiar image processing operations such as connected components, convolution, template matching, and even image translation, take on new and different forms when defined on space variant images. The present paper provides a general method for space variant image processing, based on a connectivity graph which represents the neighbor-relations in an arbitrarily structured sensor. We illustrate this approach with the following applications: (1) Connected components is reduced to its graph theoretic counterpart. We illustrate this on a logmap sensor, which possesses a difficult topology due to the branch cut associated with the complex logarithm function. (2) We show how to write local image operators in the connectivity graph that are independent of the sensor geometry. (3) We relate the connectivity graph to pyramids over irregular tessalations, and implement a local binarization operator in a 2-level pyramid. (4) Finally, we expand the connectivity graph into a structure we call a transformation graph, which represents the effects of geometric transformations in space variant image sensors. Using the transformation graph, we define an efficient algorithm for matching in the logmap images and solve the template matching problem for space variant images. Because of the very small number of pixels typical of logarithmic structured space variant arrays, the connectivity graph approach to image processing is suitable for real-time implementation, and provides a generic solution to a wide range of image processing applications with space variant sensors.This research was supported by DARPA/ONR #N00014-90-C-0049 and AFOSR Life Sciences #88-0275. Please address all correspondence to Richard S. Wallace, NYU Robotics Research Laboratory, 715 Broadway 12th Floor, New York, NY 10003. This report is copyright ©1993 by the authors. This report is a revised draft of a report published as New York University Courant Institute of Mathematical Sciences Computer Science Technical Report (No. 589 and Robotics Report No. 256), October, 1991. Last revised October.     

Real-time speckle image processing

The laser dynamic speckle is an optical phenomenon produced when a laser light is reflected from an illuminated surface undergoing some kind of activity. It allows a non-destructive process for the detection of activities that are not easily observable, such as seed viability, paint drying, bacterial activities, corrosion processes, food decomposition, fruit bruising, etc. The analysis of these processes in real time makes it possible to develop important practical applications of commercial, biological and technological interest. This paper presents a new digital system based on granular computing algorithms to characterize speckle dynamics within the time domain. The selected platform to evaluate the system is Field Programmable Gate Array (FPGA) technology. The obtained minimum clock periods and latencies enable speckle image processing with real-time constraints with a maximum throughput of about thousand 512 × 512 fps.     

Morphological image sequence processing

We present a morphological multi-scale method for image sequence processing, which results in a truly coupled spatio-temporal anisotropic diffusion. The aim of the method is not to smooth the level-sets of single frames but to denoise the whole sequence while retaining geometric features such as spatial edges and highly accelerated motions. This is obtained by an anisotropic spatio-temporal level-set evolution, where the additional artificial time variable serves as the multi-scale parameter. The diffusion tensor of the evolution depends on the morphology of the sequence, given by spatial curvatures of the level-sets and the curvature of trajectories (=acceleration) in sequence-time. We discuss different regularization techniques and describe an operator splitting technique for solving the problem. Finally we compare the new method with existing multi-scale image sequence processing methodologies.     

一种基于图像融合的图像放大处理方法

提出了一种结合图像融合与小波变换的图像插值方法.首先利用一种常用图像插值方法获得初步放大图像,再将初步放大图像进行小波分解,源图像与小波低频子图进行直方图匹配后作为新的低频子图.将初步放大图像小波分解得到的不同方向的高频系数子图结合新的低频子图作为融合图像的多分辨率金字塔.最后进行小波逆变换得到融合图像,也即放大图像.实验结果表明该方法可提高图像插值的主客观效果,峰值信噪比相对常用方法可提高2dB以上.     

The enhancement of the cell-based GIS analyses with fuzzy processing capabilities

In order to store and process natural phenomena in Geographic Information Systems (GIS) it is necessary to model the real world to form computational representation. Since classical set theory is used in conventional GIS softwares to model uncertain real world, the natural variability in the environmental phenomena cannot be modeled appropriately. Because, pervasive imprecision of the real world is unavoidably reduced to artificially precise spatial entities when the conventional crisp logic is used for modeling.An alternative approach is the fuzzy set theory, which provides a formal framework to represent and reason with uncertain information. In addition, linguistic variable concept in a fuzzy logic system is useful for communicating concepts and knowledge with human beings. FuzzyCell is a system designed and implemented to enhance commercial GIS software, namely ArcMap^® with fuzzy set theory. FuzzyCell allows users to (a) incorporate human knowledge and experience in the form of linguistically defined variables into GIS-based spatial analyses, (b) handle imprecision in the decision-making processes, and (c) approximate complex ill-defined problems in decision-making processes and classification. It provides eight membership functions, inference methods, methods for rule aggregation, operators for set operations and methods for defuzzification.The operation of FuzzyCell is presented through case studies, which demonstrate its application for classification and decision-making processes. This paper shows how fuzzy logic approach may contribute to a better representation and reasoning with imprecise concepts, which are inherent characteristics of geographic data stored and processed in GIS.     

An interactive image processing system

A multiuser multiprocessing image processing system has been developed. It is an interactive picture manipulation and enhancement facility which is capable of executing a variety of image processing operations while simultaneously controlling real-time input and output of pictures. It was designed to provide a reliable picture processing system which would be cost-effective in the commercial production environment. Additional goals met by the system include flexibility and ease of operation and modification.