• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.649-652
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• 2005

In this paper we present a detail approach of the determination of IFOV (Instantaneous Field of View) of high-resolution (l m) panchromatic satellite image over test site. IFOV is the representative measurements as the determination of the spatial resolution in remote sensed imaging system. It can be defined as some area on the ground with the particular altitude when the satellite acquires the image at any given time. Especially, spatial resolution of passive sensors primarily depends on their IFOV. The determination of IFOV goes through simple steps of procedure as followings: Firstly, the GSD (Ground Sample Distance) should be computed at each point on the geometrically corrected image. Then, The GSD is converted into the IFOV. So we are going to explain our test procedures and results.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.1
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• pp.19-27
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• 2014

Compressive sensing is an emerging sampling technique which enables sampling a signal at a much lower rate than the Nyquist rate. In this paper, we propose a novel framework based on Kronecker compressive sensing that provides multi-resolution image reconstruction capability. By exploiting the relationship of the sensing matrices between low and high resolution images, the proposed method can reconstruct both high and low resolution images from a single measurement vector. Furthermore, post-processing using BM3D improves its recovery performance. The experimental results showed that the proposed scheme provides significant gains over the conventional framework with respect to the objective and subjective qualities.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.257-266
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• 2008

This study presents an approach to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. The synthesized images should be similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme is designed to reconstruct the multispectral images at the higher resolution with as less color distortion as possible. It uses a regression model of the second order to fit panchromatic data to multispectral observations. Based on the regression model, the multispectral images at the higher spatial resolution of the panchromatic image are optimized by a quadratic programming. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement over other methods.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.525-533
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• 2008

This study presents a pansharpening method, so called FitPAN, to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. FitPAN is a modified version of the quadratic programming approach proposed in (Lee, 2008), which is designed to generate synthesized multispectral images similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme aims at reconstructing the multispectral images at the higher resolution with as less spectral distortion as possible. This study also proposes a sharpening process to eliminate some distortions appeared in the fused image of the higher resolution. It employs the Point-Jacobian MAP iteration utilizing the contextual information of the original panchromatic image. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement in both spectral and block distortion.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.709-710
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• 2024

Super resolution에서 High-frequency Details를 개선하는 것이 최근 문제이다. 기존에는 Super resolution을 Regression task로 접근하므로써 L2 Loss를 사용하여 이미지가 흐릿하게 되었다. 이를 해결하기위해, Classification task로 바꾸므로써 Cross Entropy Loss을 적용하여 Cross-entropy Super-resolution (CS)를 설계한다. CS를 통해 선명도와 Details이 개선되지만, 저주파의 CE Loss 학습으로인한 Black Artifacts가 발생한다. 그래서, L2 Loss는 저주파와 같이 큰 신호에 더 초점을 맞추므로, 성능 개선을 위해 저주파를 L2 Loss에서, 고주파를 CE Loss에서 학습시킨 Frequency-specific Cross-entropy Super-resolution (FCS)을 제안한다. 우리는 왜곡에 강하며 Human의 인식과 유사한 측정지표인 Learned Perceptual Image Patch Similarity (LPIPS)로 평가한다. 실험한 모든 데이터 셋에서 우리의 FCS는 Baseline보다 LPIPS가 약 1.7배 정도 개선되었다.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.264-272
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• 2010

Example-based super resolution is a method to restore a high resolution image from low resolution images through training and retrieval of image patches. It is not only good in its performance but also available for a single frame low-resolution image. However, its time complexity is very high because it requires lots of comparisons to retrieve image patches in restoration process. In order to improve the restoration speed, an efficient patch retrieval algorithm is essential. In this paper, we applied various high-dimensional feature retrieval methods, available for the patch retrieval, to a practical example-based super resolution system and compared their speed. As well, we propose to apply the multi-phase candidate reduction approach to the patch retrieval process, which was successfully applied in character recognition fields but not used for the super resolution. In the experiments, LSH was the fastest among conventional methods. The multi-phase candidate reduction method, proposed in this paper, was even faster than LSH: For $1024{\times}1024$ images, it was 3.12 times faster than LSH.

• Korean Journal of Materials Research
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• v.32 no.8
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• ETRI Journal
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• v.33 no.3
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• pp.366-373
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• 2011

In this paper, we propose a low-power all-digital phase-locked loop (ADPLL) with a wide input range and a high resolution time-to-digital converter (TDC). The resolution of the proposed TDC is improved by using a phase-interpolator and the time amplifier. The phase noise of the proposed ADPLL is improved by using a fine resolution digitally controlled oscillator (DCO) with an active inductor. In order to control the frequency of the DCO, the transconductance of the active inductor is tuned digitally. The die area of the ADPLL is 0.8 $mm^2$ using 0.13 ${\mu}m$ CMOS technology. The frequency resolution of the TDC is 1 ps. The DCO tuning range is 58% at 2.4 GHz and the effective DCO frequency resolution is 0.14 kHz. The phase noise of the ADPLL output at 2.4 GHz is -120.5 dBc/Hz with a 1 MHz offset. The total power consumption of the ADPLL is 12 mW from a 1.2 V supply voltage.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.25-38
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• 2016

With the increasing need for high temporal resolution satellite imagery for monitoring land surfaces, this study evaluated the temporal resolution of the NDVI composites from Geostationary Ocean Color Imager (GOCI) data. The GOCI is the first geostationary satellite sensor designed to provide continuous images over a $2,500{\times}2,500km^2$ area of the northeast Asian region with relatively high spatial resolution of 500 m. We used total 2,944 hourly images of the GOCI level 1B radiance data obtained during the one-year period from April 2011 to March 2012. A daily NDVI composite was produced by maximum value compositing of eight hourly images captured during day-time. Further NDVI composites were created with different compositing periods ranging from two to five days. The cloud coverage of each composite was estimated by the cloud detection method developed in study and then compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud product and 16-day NDVI composite. The GOCI NDVI composites showed much higher temporal resolution with less cloud coverage than the MODIS NDVI products. The average of cloud coverage for the five-day GOCI composites during the one year was only 2.5%, which is a significant improvement compared to the 8.9%~19.3% cloud coverage in the MODIS 16-day NDVI composites.

• Journal of Radiation Industry
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• v.12 no.4
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• pp.287-290
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• 2018

Nowadays, diseases related with breast are increasing rapidly and because of this high quality of resolution images is required to get clear detail specially for early detection and diagnosis. It has a tendency to use digital equipments than analog one in clinic. In this experiment, DR, CR, and Film are used for the resolution applied by AEC. Resolution phantom in DR was $7LP{\cdot}mm^{-1}$ in both verticality and horizontality. In CR, however, it was $6LP{\cdot}mm^{-1}$ in both which was lower this standard. The resolution stayed in range of standard in Film but it showed differences between $11{\sim}14LP{\cdot}mm^{-1}$ Overall, the difference of resolution was displayed Film, DR and CR, in order, which means the study is needed for more high quality of digital images.