• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.3
• /
• pp.174-181
• /
• 2011

A new registration method for IR image and EO image is proposed in this paper. IR sensor is applicable to many area because it absorbs thermal radiation energy unlike EO sensor does. However, IR sensor has difficulty to extract and match features due to low contrast compared to EO image. In order to register both images, we used modified SIFT(Scale Invariant Feature Transform) and block processing to increase feature distinctiveness. To remove outlier, we applied RANSAC(RANdom SAample Concensus) for each block. Finally, we unified matching features into single coordinate system and remove outlier again. We used 3~5um range IR image, and our experiment result showed good robustness in registration with IR image.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.12 no.4
• /
• pp.500-507
• /
• 2009

This paper presents a novel method which can detect a target in IR image for active protection system. The target in IR image for the active protection system is small, moreover it moves with enormous speed. The proposed algorithm is comprised of robust clutter rejection methods and target optimized detection algorithms for small target, and an advanced method of selecting a final target position in target area, it can work in some milliseconds. The proposed algorithm provides the active protective system with more correct positions than those of radar, so that helps the active protection system can defense all threats with the utmost precision.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.2
• /
• pp.162-169
• /
• 2019

This paper describes a thermal image processing algorithm for uncooled type TEC-less IR detector which is applicable to fire control system of small arms. We implemented a real-time gain and offset compensation algorithm based on polynomial approximation from the raw dataset which is acquired by two reference temperature of blackbody from various FPA(Focal Plane Array) temperature. Through the experiment, we analyzed the output characteristics of detector's raw-data and compared IR image quality to traditional non-uniformity correction method. It shows that the proposed method works well in all FPA temperature range with low residual non-uniformity.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.1
• /
• pp.33-39
• /
• 2018

In this paper, We studied the possibility of SoC(System On Chip) design using infrared image processing IP(Intellectual Property). And, we studied NUC(Non Uniformity Correction), BPR(Bad Pixel Recovery), and CEM(Contrast Enhancement) processing, the infrared image processing algorithm implemented by IP. We showed the logic and timing diagram implemented through the hardware block designed based on each algorithm. Each algorithm was coded as RTL(Register Transfer Level) using Verilog HDL(Hardware Description Language), ALTERA QUARTUS synthesis, and programed in FPGA(Field Programmable Gated Array). In addition, we have verified that the image data is processed at each algorithm without any problems by integrating the infrared image processing algorithm. Particularly, using the directly manufactured electronic board, Processor, SRAM, and FLASH are interconnected and tested and the verification result is presented so that the SoC type can be realized later. The infrared image processing IP proposed and verified in this study is expected to be of high value in the future SoC semiconductor fabrication. In addition, we have laid the basis for future application in the camera SoC industry.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.545-555
• /
• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• Journal of Astronomy and Space Sciences
• /
• v.27 no.4
• /
• pp.309-318
• /
• 2010

We introduce a technical approach for reducing three-dimensional infrared (IR) spectroscopic data generated by integral field spectroscopy or slit-scanning observations. The first part of data reduction using IRAF presents a guideline for processing spectral images from long-slit IR spectroscopy. Multichannel image reconstruction, Image Analysis and Display (MIRIAD) is used in the later part to construct and analyze the data cubes which contain spatial and kinematic information of the objects. This technic has been applied to a sample data set of diffuse 2.1218 ${\mu}m$ $H_2$ 1-0 S(1) emission features observed by slit-scanning around Sgr A East in the Galactic center. Details of image processing for the high-dispersion infrared data are described to suggest a sequence of contamination cleaning and distortion correction. Practical solutions for handling data cubes are presented for survey observations with various configurations of slit positioning.

• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.282-285
• /
• 2006

This paper analyzed the characteristics of the Lossy JPEG of the meteorological satellite image, and analyzed the quality of the Lossy JPEG compression, which is proper for the LRIT(Low Rate Information Transmission) to be serviced to the SDUS(Small-scale Data Utilization Station) system of the COMS(Communication, Oceans, Meteorological Satellite). Since COMS is to start running after 2008, we collected the data of the MTSAT-1R(Multi-functional Transport Satellite -1R) for analysis, and after forming the original image to be used to LRIT by each channel and time zone of the satellite image data, we set the different quality with the Lossy JPEG compression, and compressed the original data. For the characteristic analysis of the Lossy JPEG, we measured PSNR(Peak Signal to Noise Rate), compression rate and the time spent in compression following each quality of Lossy JPEG compression. As a result of the analysis of the satellite image data of the MTSAT-1R, the ideal quality of the Lossy JPEG compression was found to be 90% in the VIS Channel, 85% in the IR1 Channel, 80% in the IR2 Channel, 90% in the IR3 Channel and 90% in the IR4 Channel.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.4
• /
• pp.246-253
• /
• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of Radiation Protection and Research
• /
• v.35 no.1
• /
• pp.26-33
• /
• 2010

A computed Radiography (CR) system by use of reusable Image Plate (IP) offers a convenient and reliable way to replace a conventional film-screen system for NDT (non-destructive testing) field. The quality of a radiography to detect a defect of welded objects depends on the procedure embracing several factors such as measurement conditions, image plate type/class, radiation energy, radiation type, and source to image plate distance. Also, the ability of images to detect a flaw reduces with increasing object thickness. In the study, the properties of gamma ray source were summarized for NDT field and inspection images of CR image system manufactured by FUJI were acquired using $^{75}Se$ and $^{192}Ir$ with welded objects. We analyzed the gray scale of hole defect image by using XCAP image processing program and calculated the image contrast and SNR in definition. Also the sesitivities of image quality indicator(IQI) were calculated for hot and cooling tube image of $^{75}Se$ and $^{192}Ir$.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.11
• /
• pp.73-81
• /
• 2016

In general, infrared images have less sharpness and image details than visible images. So, the prior image upscaling methods are not effective in the infrared images. In order to solve this problem, this paper proposes an algorithm which initially up-scales an input infrared (IR) image by using adaptive dynamic range encoding (ADRC)-based super-resolution (SR) method, and then fuses the result with the corresponding visible images. The proposed algorithm consists of a up-scaling phase and a fusion phase. First, an input IR image is up-scaled by the proposed ADRC-based SR algorithm. In the dictionary learning stage of this up-scaling phase, so-called 'pre-emphasis' processing is applied to training-purpose high-resolution images, hence better sharpness is achieved. In the following fusion phase, high-frequency information is extracted from the visible image corresponding to the IR image, and it is adaptively weighted according to the complexity of the IR image. Finally, a up-scaled IR image is obtained by adding the processed high-frequency information to the up-scaled IR image. The experimental results show than the proposed algorithm provides better results than the state-of-the-art SR, i.e., anchored neighborhood regression (A+) algorithm. For example, in terms of just noticeable blur (JNB), the proposed algorithm shows higher value by 0.2184 than the A+. Also, the proposed algorithm outperforms the previous works even in terms of subjective visual quality.