Optimized algorithm module for large volume remote sensing image processing system

Nowadays, the increase of quality and resolution of remote sensing image will lead to the data's capacity extremely increase; A single computer, even a very expensive minicomputer or a PC server, is difficult to meet the disk space, memory space and processing time requirements when faced with this situation. Cloud computing can give us a new resolution to these problems. In this study, we proposed a Hadoop-based remote sensing image processing system (HBRSIPS), on the one hand, it re-defines a storage structure to adapt the characters of remote sensing images, on the other hand, We construct Java program classes based on MapReduce architecture, remote sensing image processing algorithms can be easily applied to the cloud computing environment. The experiments show, the proposed system can save remote sensing image into Hadoop's distribute storage environment and algorithms based on cloud environment were faster than single process algorithm.

This article made a detailed introduction to the design and implementation of image processing algorithm framework, with its focus on data-driven, image processing algorithm, UI and processing dialog optimization. Implemented by the C++ programming language, the final framework provides a new way and reference to the Customizable development of remote sensing image processing algorithm.The remote sensing image processing system is a set of technologies and methodoloies that can add enormous value in handling the problem of the earth sciences and related fields.Most practitioners are, primarily, professionals in fields such as hydrology, agriculture, engineering, forestry, geology and urban studies; giving them the writing remote sensing processing algorithm skills considerably enhances their expertise and capablities.

This paper mainly studies the low-altitude UAV (unmanned aerial vehicle) remote sensing digital processing system. The system is mainly a set of digital remote sensing image processing system developed for the data acquired by the system. First, it describes the basic principles of photogrammetry on which the system is based, and mainly explains the advantages of the beam method area network adjustment. This article briefly describes the common methods of surveying technology for power transmission and transformation lines. UAV aerial survey technology and GIS platform are applied to electric power surveys; the UAV aerial survey data are used as the data source to establish a GIS database, and the detailed system design of the structure, interface, and database of the electric power survey geographic information system is carried out. The secondary development realizes the main functions of the system such as data management, spatial query, surface analysis, and spatial analysis, facilitating scientific information management and comprehensive analysis, providing auxiliary decision-making, and bringing new methods to electric power surveys. Secondly, this article points out that the system uses an ordinary digital camera with small image size and large distortion. This article is based on the basic principles of photogrammetry, and it is worthwhile to use navigation GPS. Thirdly, the accuracy of the aerial triangulation results of the system and the accuracy of the digital orthophoto were analyzed through a large amount of data.

Aircraft detection is the challenging task in optical remote sensing image processing system. Due to the shadow affection, the accuracy of detection results getting lower. Therefore, a method of the land aircraft detection which based on shadow affection in optical remote sensing image has been proposed. First, utilizing the anisotropic diffusion method to filter the noise in image, and analyzing the characteristics of gray-scale image to locate shadow regions and candidate target areas. Then, segmentation is performed on the filter able image to highlight the target. Next, we use the morphology operation to strengthen the target area connectedness. Finally, making full use of the aircraft features for target representation, it will reflecting the aircraft target prior-knowledge, at the same time, the accuracy of detection results will be greatly enhancement. Experimental results reveal the feasibility and validity of the proposed approach in detection aircrafts in optical remote sensing images.

Image process is one among most growing analysis space these days and currently it's greatly integrated with the medical and biotechnology field. Image process will be used to analyze different medical and MRI pictures to induce the abnormality within the image. In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images and experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE) proposes associate economical K-means clump algorithm underneath Morphological Image process (MIP). Medical Image segmentation deals with segmentation of tumour in CT and MR pictures for improved quality in diagnosis. It is an important method and a difficult drawback because of noise presence in input pictures throughout image analysis. It's required for applications involving estimation of the boundary of associate object, classification of tissue abnormalities, form analysis, contour detection. Segmentation determines because the method of dividing associate image into disjoint consistent regions of a medical image. The amount of resources needed to explain massive set of information is simplified and is chosen for tissue segmentation. In our paper, this segmentation is administrated victimization K-means clump algorithm for higher performance. This enhances the growth boundaries more and is extremely fast in comparison to several other clustering algorithms. This paper produces the reliable results that area unit less sensitive to error. Keywords: Magnetic resonance image, Morphological Image processing, Image segmentation, K-means, Morphological operations, Fuzzy, PSNR, RMSE.

A This paper is the focus of the dissertation and focuses on some commonly used image filtering algorithms. The commonly used image filters are median filter, Gaussian filter and edge detection algorithm. In this chapter, the FPGA implementation of grayscale processing module is studied first, then median filtering and Gaussian filtering are studied, and a fast median filtering algorithm and displacement-optimized Gaussian filtering algorithm are proposed, and the feasibility of the algorithms is demonstrated by simulation experiments. Then the edge detection algorithm is studied and the FPGA method to implement each algorithm is designed, in which an improved algorithm is proposed for Laplace algorithm to improve the detection accuracy, and finally the Sobel operator is also shifted optimized to save the hardware resources of the system.

Image filtering involves taking a digital image and producing a new image from it. In software packages such as Adobe's Photoshop, image filters are used to produce artistic versions of original images. Such software usually includes hundreds of different image filtering algorithms, each with many fine-tuneable parameters. While this freedom of exploration may be liberating to artists and designers, it can be daunting for less experienced users. Photoshop provides image filter browsing technology, but does not yet enable the construction of a filter which produces a reasonable approximation of a given filtered image from a given original image. We investigate here whether it is possible to automatically evolve an image filter to approximate a target filter, given only an original image and a filtered version of the original. We describe a tree based representation for filters, the fitness functions and search techniques we employed, and we present the results of experimentation with various search setups. We demonstrate the feasibility of evolving image filters and suggest new ways to improve the process.

The conventional algorithm of the image filtering is basis on the assumption that the image is stationary. The algorithm based on this model can reduce the noise in the image, but it can also lose the high frequency information. Therefore, the model of the image is improved, so that effect of the image filtering algorithm is improved. This paper assumes that the model of image is the local stationary Gauss model by the analysis of the original infrared image. Furthermore, this paper thinks the noise of the infrared image is not additive noise, but is multiplicative noise. It is the signal-dependent noise. Therefore, the infrared image filtering algorithm based on the minimum mean-square error estimation is devised. Final, this algorithm is compared with the infrared image filtering algorithm based on the maximum likelihood estimation. This algorithm can not only reduce the noise of the infrared image but also reserve the high frequency information. Especially, the algorithm does not lose the point target in the infrared image.

This paper presents a summary of operations of the logarithmic image processing (LIP) model and its applications. The LIP model is a mathematical framework which provides a set of generalised version of addition, subtraction, multiplication, convolution and so on, for signal processing. First, we briefly describe the LIP model and point out its distinctive properties for image processing. Then we summarize the current applications of the LIP model. A novel image filtering algorithm and its extension to multiscale processing along with the LIP model based Sobel operator for edge detection are discussed in detail. Finally, we suggest further research area of the LIP model. >

The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models.

Remove adversarial perturbations with linear and nonlinear image filters

Two different program systems can be used when planning squint surgery. In the first system's algorithms (Robinson-Miller-Simonsz) are modelled the mechanical and innervational laws that govern eye movements (ophthalmotrope program). With ocular deviations and surgical procedures as input the program estimates postoperative deviations. The second program (Rüssmann-Konen) mimics any surgeon's planning of squint surgery using a defined decision tree and an appropriate number of mm-degree-relations (planning program). If ocular deviations, axial length, monocular excursions, and head posture are input the program proposes a surgical procedure. I combined both programs to enable the ophthalmotrope program to evaluate the propositions of the planning program. I translated Simonsz' version 1/1989 of Robinson's program from FORTRAN to the programming language of my planning program (Turbo BASIC for IBM-PC) and connected both, the former becoming a module of the latter. Thus data entered into the planning program as well as the surgical procedure proposed may be immediately processed by the ophthalmotrope program to compute the presumed postoperative result. Several program loops allow a wide variation of data and procedures in successive program runs. Turbo BASIC being somewhat slower than FORTRAN quick computers (INTEL 80486, Pentium) are necessary lest processing lasts more than 1 minute. A concomitant esotropia of 20 degrees and a head posture with elevation of the chin of 20 degrees are used to illustrate program properties. In the first case the planning program proposes a reasonable recess-resect-procedure 6 mm each, which is estimated to give a very slight overcorrection by the ophthalmotrope module. In the second case the planning module proposes a gaze shift procedure (Kestenbaum) on the vertical recti, which--in good agreement with my own experience--is estimated to produce an A-phenomenon of 19 degrees with an incyclotropia up to 30 degrees by the ophthalmotrope module. If an anterior margin recession of the superior oblique and an anterior margin tuck of the inferior oblique are added to the gaze shift procedure in both eyes neither any phenomenon nor any significant deviation are predicted by the ophthalmotrope module. The combination of a planning module with an ophthalmotrope module in one program considerably enhances the usefulness of both.

The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

The Society of Biomolecular Imaging and Informatics First Annual Conference

Expert Systems for Image Processing, Analysis, and Recognition: Declarative Knowledge Representation for Computer Vision