• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.16-23
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• 2011

Muzzle flash based on gunnery is the target that has huge energy. So, gunnery target in a long range over xx km is distinguishable in the IR(infrared) images, on the other hand, is not distinguishable in the CCD images. In this paper, we propose the classification method of gunnery targets in a infrared images and in a long range. The energy from gunnery have an effect on varous pixel values in infrared images as a property of infrared image sensor, distance, and atmosphere, etc. For this reason, it is difficult to classify gunnery targets using pixel values in infrared images. In proposed method, we take the profile of pixel values using high performance infrared sensor, and classify gunnery targets using modeling GMM and shape of profile. we experiment on the proposed method with infrared images in the ground and aviation. In experimental result, the proposed method provides about 93% classification rate.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.207-213
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• 2013

An infrared image synthetic method is proposed for infrared system simulation. The synthesis image uses a background IR image captured from real scene and a target IR modeling image. The radiances related with maximum and minimum temperatures of the background and target images are calculated from the Planck's blackbody equation. Based on them, the background and target images are compensated and synthesized. The proposed method is simulated and the IR target images are generated by RadThermIR software.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.205-215
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• 2011

Films are sensitive to ultraviolet rays and in contrast, digital camera sensors are extremely sensitive to infrared rays due to the differences in spectral characteristics. As a result, all digital cameras that use CCD or CMOS are equipped with IR Cut-Off Filter on the overall sensor. Complete block out of infrared rays is ideal, but the actual experiment results showed that infrared rays were not being blocked out completely. Infrared permeability was also different for each camera. Therefore, this study aims to analyze the effect of the minute quantities of infrared rays, which get transmitted due to mechanical properties of IR Cut-Off Filters that are installed on digital cameras, on digital picture images. The results obtained by carrying out a comparative analysis of a UV Filter (infrared transmitting state) and a UV-IR Filter (infrared blocked out state) are as follows. It was confirmed that the minute quantities of infrared rays do affect dynamic range and resolution to some extent, despite the little or no difference in noise and color reproduction.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.141-147
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• 2017

Recently there has been an increasing demand for developing a domestic software (S/W) for infrared signature generation to prevent technology leakage and match the domestic operating environment. In this study, we developed a S/W for infrared signature generation and presented its structures and functions for creating and analyzing the IR images of designated spectral bands. The proposed S/W generates IR images of an object through calculations of surface temperatures and IR signals including the self-emitted, surface reflected and path dependent radiances. Moreover, the proposed S/W includes the features of infrared threat analyses from the generated IR images including the infrared contrast radiant intensity (CRI), detection ranges or detection probability analyses, unlike the imported, commercial infrared signature generation S/W.

• Journal of Applied and Pure Mathematics
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• v.5 no.5_6
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• pp.389-406
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• 2023

In the pursuit of enhancing image fusion techniques, this research presents a novel approach for fusing multimodal images, specifically infrared (IR) and visible (VIS) images, utilizing a combination of partial differential equations (PDE) and discrete cosine transformation (DCT). The proposed method seeks to leverage the thermal and structural information provided by IR imaging and the fine-grained details offered by VIS imaging create composite images that are superior in quality and informativeness. Through a meticulous fusion process, which involves PDE-guided fusion, DCT component selection, and weighted combination, the methodology aims to strike a balance that optimally preserves essential features and minimizes artifacts. Rigorous evaluations, both objective and subjective, are conducted to validate the effectiveness of the approach. This research contributes to the ongoing advancement of multimodal image fusion, addressing applications in fields like medical imaging, surveillance, and remote sensing, where the marriage of IR and VIS data is of paramount importance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2749-2763
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• 2021

The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is proposed to optimize the Retinex model. With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion outcome is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high contrast targets in IR images. Experiment statistic shows that compared to some advanced approaches, the proposed method has superiority on detail preservation and visual quality.

• Ceramist
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• v.10 no.3
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• pp.55-66
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• 2007

Infrared Scanning Near-field Optical Microscopy (IR-SNOM) is an extremely powerful analytical instrument since it combines IR spectroscopy's high chemical specificity with SNOM's high spatial resolution. In order to do this in the infrared, specialty chalcogenide glass fibers were fabricated and their ends tapered to generate SNOM probes. The fiber tips were installed in a modified near field microscope and both inorganic and biological samples illuminated with the tunable output from a free-electron laser located at Vanderbilt University. Both topographical and IR spectral images were simultaneously recorded with a resolution of ${\sim}50\;nm$ and ${\sim}100\;nm$, respectively. Unique spectroscopic features were identified in all samples, with spectral images exhibiting resolutions of up to ${\lambda}/60$, or at least 30 times better than the diffraction limited lens-based microscopes. We believe that IR-SNOM can provide a very powerful insight into some of the most important bio-medical research topics.

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

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1073-1082
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• 2023

Thermal sensors, also called thermal infrared wavelength sensors, measure temperature based on the intensity of infrared signals that reach the sensor. The infrared signals recognized by the sensor include infrared wavelength(0.7~3.0㎛) and radiant infrared wavelength(3.0~100㎛). Infrared(IR) wavelengths are divided into five bands: near infrared(NIR), shortwave infrared(SWIR), midwave infrared(MWIR), longwave infrared(LWIR), and far infrared(FIR). Most thermal sensors use the LWIR to capture images. Thermal sensors measure the temperature of the target in a non-contact manner, and the data can be affected by the sensor's viewing angle between the target and the sensor, the amount of atmospheric water vapor (humidity), air temperature, and ground conditions. In this study, the characteristics of three thermal imaging sensor models that are widely used for observation using unmanned aerial vehicles were evaluated, and the optimal application field was determined.

• ETRI Journal
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• v.37 no.5
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• pp.1023-1031
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• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.