Super-resolution Imaging of Telescopic Systems based on Optical-neural Network Joint Optimization

Optical telescopes are an important tool for acquiring optical information about distant objects,and resolution is an important indicator that measures the ability to observe object details.However,due to the effects of system aberration,atmospheric seeing,and other factors,the observed image of ground-based telescopes is often degraded,resulting in reduced resolution.This paper proposes an optical-neural network joint optimization method to improve the resolution of the observed image by co-optimizing the point-spread function(PSF)of the telescopic system and the image super-resolution(SR)network.To improve the speed of image reconstruction,we designed a generative adversarial net(LCR-GAN)with light parameters,which is much faster than the latest unsupervised networks.To reconstruct the PSF trained by the network in the optical path,a phase mask is introduced.It improves the image reconstruction effect of LCR-GAN by reconstructing the PSF that best matches the network.The results of simulation and verification experiments show that compared with the pure deep learning method,the SR image reconstructed by this method is rich in detail and it is easier to distinguish stars or stripes.

Application of Morphological Filter in Rosette Scanning Sub-Imaging System

To restore the sub image in a rosette scanning system and provide target recognition system with a low distorted image, the sub image is processed with morphological filters. Morphological filter can process rosette scanning sub images more effectively. It can restore the original area and shape of an object effectively, and keep the energy information of the object. To process sub images got by a rosette scanning system, morphological filter is more effective than traditional low pass filter.

APPLICATION OF IMAGE PROCESSING TECHNIQUE IN SPM SYSTEM

Up to now the imported commercial scanning probe microscope(SPM) has not an automatic error correcting and reducing system.In this paper a software system is presented to solve this problem.This software system gives the average distance between the centers of mass of two adjacent atoms on the same horizontal line and its mean square root as well as the atoms shape and center of mass by filtering the measured image of a standard sample-highly oriented pyrolysis graphite(HOPG).This system forms the basis of SPMs automatic measurement error correcting.

Surface Inspection System for Cold Rolled Strips Based on Image ProcessingTechnique

A new surface inspection system for cold rolled strips based on image processing is introduced. The system is equipped withtwo different illumination structures and CCD matrix cameras. The structure and image processing of the inspection system are described. Some efficient algorithms for image processing and classification are presented. The system is tested with strip samples fromcold rolling plants. The results show that the system can detect and recognize six common defects of cold rolled strips successfully.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Parallel Image Processing Technology of Surface Detection System

To improve image processing speed and detection precision of a surface detection system on a strip surface,based on the analysis of the characteristics of image data and image processing in detection system on the strip surface,the design of parallel image processing system and the methods of algorithm implementation have been studied. By using field programmable gate array(FPGA) as hardware platform of implementation and considering the characteristic of detection system on the strip surface,a parallel image processing system implemented by using multi IP kernel is designed. According to different computing tasks and the load balancing capability of parallel processing system,the system could set different calculating numbers of nodes to meet the system's demand and save the hardware cost.

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction.The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images.The method included the following steps:extracting edges,segmenting boundary arcs,fitting circles and excluding false image stars.The proposed method was tested using 200 ISS images.Preliminary experimental results show that it can remove the false image stars in more than 95%of ISS images with disk-resolved objects in a fully automatic manner,i.e.,outperforming the traditional circle detection based on Circular Hough Transform(CHT)by 17%.In addition,its speed is more than twice as fast as that of the CHT method.It is also more robust(no manual parameter tuning is needed)when compared with CHT.The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure.Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method.It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.

Spatial resolution and image processing for pinhole camera-based X-ray fluorescence imaging: a simulation study

Spatial resolution and image-processing methods for full-field X-ray fluorescence(FF-XRF)imaging using X-ray pinhole cameras were studied using Geant4simulations with different geometries and algorithms for image reconstruction.The main objectives were:(1)calculating the quantum efficiency curves of specific cameras,(2)studying the relationships between the spatial resolution and the pinhole diameter,magnification,and camera binning value,and(3)comparing image-processing methods for pinhole camera systems.Several results were obtained using a point and plane source as the X-ray fluorescence emitter and an array of 100×100 silicon pixel detectors as the X-ray camera.The quantum efficiency of a back-illuminated deep depletion(BI-DD)structure was above 30%for the XRF energies in the 0.8–9 keV range,with the maximum of 93.7%at 4 keV.The best spatial resolution of the pinhole camera was 24.7μm and 31.3 lp/mm when measured using the profile function of the point source,with the diameter of 20μm,magnification of 3.16,and camera bin of 1.A blind deconvolution algorithm with Gaussian filtering performed better than the Wiener filter and Richardson iterative methods on FF-XRF images,with the signal-to-noise ratio of 7.81 dB and improved signalto-noise ratio of 7.24 dB at the diameter of 120μm,magnification of 1.0,and camera bin of 1.

An iterative wavefront sensing algorithm for high-contrast imaging systems

Wavefront sensing from multiple focal plane images is a promising technique for high-contrast imaging systems.However,the wavefront error of an optics system can be properly reconstructed only when it is very small.This paper presents an iterative optimization algorithm for the direct measurement of large static wavefront errors from only one focal plane image.We first measure the intensity of the pupil image to get the pupil function of the system and acquire the aberrated image on the focal plane with a phase error that will be measured.Then we induce a dynamic phase on the tested pupil function and calculate the associated intensity of the reconstructed image on the focal plane.The algorithm will then try to minimize the intensity difference between the reconstructed image and the aberrated test image in the focal plane,where the induced phase is a variable of the optimization algorithm.The simulation shows that the wavefront of an optical system can theoretically be reconstructed with high precision,which indicates that such an iterative algorithm may be an effective way to perform wavefront sensing for high-contrast imaging systems.

Study on the image processing of laser vision seam tracking system

Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.

An Optimized and Hybrid Framework for Image Processing Based Network Intrusion Detection System

The network infrastructure has evolved rapidly due to the everincreasing volume of users and data.The massive number of online devices and users has forced the network to transform and facilitate the operational necessities of consumers.Among these necessities,network security is of prime significance.Network intrusion detection systems(NIDS)are among the most suitable approaches to detect anomalies and assaults on a network.However,keeping up with the network security requirements is quite challenging due to the constant mutation in attack patterns by the intruders.This paper presents an effective and prevalent framework for NIDS by merging image processing with convolution neural networks(CNN).The proposed framework first converts non-image data from network traffic into images and then further enhances those images by using the Gabor filter.The images are then classified using a CNN classifier.To assess the efficacy of the recommended method,four benchmark datasets i.e.,CSE-CIC-IDS2018,CIC-IDS-2017,ISCX-IDS 2012,and NSL-KDD were used.The proposed approach showed higher precision in contrast with the recent work on the mentioned datasets.Further,the proposed method is compared with the recent well-known image processing methods for NIDS.

Data-driven polarimetric imaging: a review

This study reviews the recent advances in data-driven polarimetric imaging technologies based on a wide range of practical applications.The widespread international research and activity in polarimetric imaging techniques demonstrate their broad applications and interest.Polarization information is increasingly incorporated into convolutional neural networks(CNN)as a supplemental feature of objects to improve performance in computer vision task applications.Polarimetric imaging and deep learning can extract abundant information to address various challenges.Therefore,this article briefly reviews recent developments in data-driven polarimetric imaging,including polarimetric descattering,3D imaging,reflection removal,target detection,and biomedical imaging.Furthermore,we synthetically analyze the input,datasets,and loss functions and list the existing datasets and loss functions with an evaluation of their advantages and disadvantages.We also highlight the significance of data-driven polarimetric imaging in future research and development.

High performance reconfigurable hardware system for real-time image processing

A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time image processing and can get satisfactory effect of image recognition.

Computer Vision Technology for Fault Detection Systems Using Image Processing

In the period of Industries 4.0,cyber-physical systems(CPSs)were a major study area.Such systems frequently occur in manufacturing processes and people’s everyday lives,and they communicate intensely among physical elements and lead to inconsistency.Due to the magnitude and importance of the systems they support,the cyber quantum models must function effectively.In this paper,an image-processing-based anomalous mobility detecting approach is suggested that may be added to systems at any time.The expense of glitches,failures or destroyed products is decreased when anomalous activities are detected and unplanned scenarios are avoided.The presently offered techniques are not well suited to these operations,which necessitate information systems for issue treatment and classification at a degree of complexity that is distinct from technology.To overcome such challenges in industrial cyber-physical systems,the Image Processing aided Computer Vision Technology for Fault Detection System(IM-CVFD)is proposed in this research.The Uncertainty Management technique is introduced in addition to achieving optimum knowledge in terms of latency and effectiveness.A thorough simulation was performed in an appropriate processing facility.The study results suggest that the IM-CVFD has a high performance,low error frequency,low energy consumption,and low delay with a strategy that provides.In comparison to traditional approaches,the IM-CVFD produces a more efficient outcome.

Passive ranging and a three-dimensional imaging system through wavefront coding

A single-image passive ranging and three-dimensional(3 D)imaging system with chiral phase encoding was proposed in 2011[Opt.Lett.36,115(2011)].A new theoretical analysis of the system in space domain is presented in this paper.We deduce the analytic relationships between the object distance and the point spread function,and between the object distance and the encoded image,respectively.Both the point spread function and the processed spectrum of the encoded image have two spots,which will rotate with the variation of the object distance.Then the depth map is extracted from the encoded image and it can be used to set up 3 D images.The theoretical analysis is verified by a wavefront coding system with a chiral phase which is generated by a phase-only liquid-crystal spatial light modulator.The phase generated by the liquid-crystal spatial light modulator is more flexible than the fixed phase mask and can be adjusted in real time.It is especially suitable for observing the object with a large depth of field.

An FPGA-based real-time image processing system

This paper analyzes the current difficulties encountered in on-line inspection systems of strip surface quality, specifically relating to problems with real-time processing of huge amounts of data. To address this need, this paper describes an FPGA-based high-speed image processing module with both hardware and software aspects. Improving these two aspects together will help the system achieve real-time processing of massive image data, and simplifies the architecture of the strip surface quality on-line inspection system.

Animal Classification System Based on Image Processing &Support Vector Machine

This project is mainly focused to develop system for animal researchers & wild life photographers to overcome so many challenges in their day life today. When they engage in such situation, they need to be patiently waiting for long hours, maybe several days in whatever location and under severe weather conditions until capturing what they are interested in. Also there is a big demand for rare wild life photo graphs. The proposed method makes the task automatically use microcontroller controlled camera, image processing and machine learning techniques. First with the aid of microcontroller and four passive IR sensors system will automatically detect the presence of animal and rotate the camera toward that direction. Then the motion detection algorithm will get the animal into middle of the frame and capture by high end auto focus web cam. Then the captured images send to the PC and are compared with photograph database to check whether the animal is exactly the same as the photographer choice. If that captured animal is the exactly one who need to capture then it will automatically capture more. Though there are several technologies available none of these are capable of recognizing what it captures. There is no detection of animal presence in different angles. Most of available equipment uses a set of PIR sensors and whatever it disturbs the IR field will automatically be captured and stored. Night time images are black and white and have less details and clarity due to infrared flash quality. If the infrared flash is designed for best image quality, range will be sacrificed. The photographer might be interested in a specific animal but there is no facility to recognize automatically whether captured animal is the photographer’s choice or not.

Image Restoration of Depth of Field Extension Imaging System Based on Genetic Algorithm

Genetic algorithm is a search algorithm based on genetic mechanism and natural selection. It has been widely applied to research fields including image processing field. The paper improves standard genetic algorithm and improves the arithmetic speed of the algorithm, which achieves better image restoration effect. And the paper compares the image restoration quality of traditional algorithm, standard genetic algorithm and improved genetic algorithm to prove the feasibility of applying genetic algorithm to image restoration.

he Design and Implementation of Medical Image Processing System based on DICOM Format

In order to fast transmission and processing of medical images and do not need to install client and plug-ins, the paper designed a kind of medical image reading system based on BS structure. This system improved the existing IWEB in the framework of PACS client image processing, medical image based on the service WEB completion port model. To realize the fast loading images with high concurrency, compared with the traditional WEB PACS, this system has the advantages of no client without plug-in installation, at the same time in the transmission and processing performance image has been greatly improved.

MICROCOMPUTER PICTURE PROCESSING SYSTEM OF CAST-IRON METALLOGRAPHIC STRUCTURE

The development of computer picture processing technique in metallography analysis is dealed with and a picture processing procedure fit to metallography analysis is developed. The image processing of nodular cast iron is carried out with this system