• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.146-153
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• 2018

This paper presents a superresolution demosaicing technique that can restore high-resolution color image from differently blurred low resolution images in Bayer domain. The proposed superresolution demosaicing algorithm uses an aperture mask wheel to get differently blurred low resolution images, so we just need to estimate point spread function at each frame. In addition, it does not require image registration because there is no translational motion between low resolution images. By using a rotatable aperture mask wheel, consecutive captured images provide sufficiently exclusive information for superresolution. Therefore, the proposed method can reduce the registration error between the low-resolution image as well as the calculation amount for superresolution restoration. The existing lens system of the camera can be extended to obtain a superresolution image by only adding an rotatable aperture mask wheels. Finally, in order to verify the performance of the proposed system, experimental results are performed. The proposed method showed the significant improvements in the sense of spatial and color resolution.

• Journal of Broadcast Engineering
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• v.19 no.1
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• pp.109-117
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• 2014

This paper presents a superresolution restoration technique that can restore high-resolution images from differently blurred low resolution images rather than using the motion information between low-resolution images. In order to restore the super-resolution image the rotatable aperture mask lens system is proposed. The proposed technique does not need to estimate point spread function at each frame. In addition, it does not require image registration because there is no global translational motion between low resolution images. By using a rotatable rectangular aperture, two consecutive captured images provide sufficiently exclusive information for superresolution. Therefore, the proposed method can reduce the registration error between the low-resolution image as well as the calculation amount for superresolution restoration. The existing lens system of the camera can be extended to obtain a superresolution image by only adding an rotatable rectangular aperture mask. Finally, in order to verify the performance of the proposed system, experimental results are performed. By comparing with the existing superresolution methods, the proposed method showed the significant improvements in the sense of spatial resolution.

• Journal of the Optical Society of Korea
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• v.20 no.1
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• pp.101-106
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• 2016

A pupil filter is a useful technique for modifying the light intensity distribution near the focus of an optical system to realize depth of field (DOF) extension and superresolution. In this paper, we proposed a new design of the phase only pupil filter by using Zernike polynomials. The effect of design parameters of the new filters on DOF extension and superresolution are discussed, such as defocus Strehl ratio (S.R.), superresolution factor (G) and relative first side lobe intensity (M). In comparison with the other two types of pupil filters, the proposed filter presents its advantages on controlling both the axial and radial light intensity distribution. Finally, defocused imaging simulations are carried out to further demonstrate the effectiveness and superiority of the proposed pupil filter on DOF extension and superresolution in an optical imaging system.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.676-686
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• 2015

A 4D light field image is represented in traditional 2D spatial domain and additional 2D angular domain. The 4D light field has a resolution limitation both in spatial and angular domains since 4D signals are captured by 2D CMOS sensor with limited resolution. In this paper, we propose a dictionary learning-based superresolution algorithm in 4D light field domain to overcome the resolution limitation. The proposed algorithm performs dictionary learning using a large number of extracted 4D light field patches. Then, a high resolution light field image is reconstructed from a low resolution input using the learned dictionary. In this paper, we reconstruct a 4D light field image to have double resolution both in spatial and angular domains. Experimental result shows that the proposed method outperforms the traditional method for the test images captured by a commercial light field camera, i.e. Lytro.

• Current Optics and Photonics
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• v.2 no.2
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• pp.153-164
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• 2018

In semiconductor manufacturing, critical dimensions indicate the features of patterns formed by the semiconductor process. The purpose of measuring critical dimensions is to confirm whether patterns are made as intended. The deposition process for an organic light emitting diode (OLED) forms a luminous organic layer on the thin-film transistor electrode. The position of this organic layer greatly affects the luminescent performance of an OLED. Thus, a system for measuring the position of the organic layer from outside of the vacuum chamber in real-time is desired for monitoring the deposition process. Typically, imaging from large stand-off distances results in low spatial resolution because of diffraction blur, and it is difficult to attain an adequate industrial-level measurement. The proposed method offers a new superresolution single-image using a conversion formula between two different optical systems obtained by a deep learning technique. This formula converts an image measured at long distance and with low-resolution optics into one image as if it were measured with high-resolution optics. The performance of this method is evaluated with various samples in terms of spatial resolution and measurement performance.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.491-492
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• 2014

• ETRI Journal
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• v.35 no.3
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• pp.361-370
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• 2013

In this paper, a packet-reduced ranging method using a superresolution time of arrival estimation algorithm for a chirp-based real-time locating system is presented. A variety of ranging methods, such as symmetric double-sided two-way ranging (SDS-TWR), have been proposed to remove the time drift due to the frequency offset using extra ranging packets. Our proposed method can perform robust ranging against the frequency offset using only two ranging packets while maintaining almost the same ranging accuracy as them. To verify the effectiveness of our proposed algorithm, the error performance of our proposed ranging method is analyzed and compared with others. The total ranging performance of TWR, SDS-TWR, and our proposed TWR are analyzed and verified through simulations in additive white Gaussian noise and multipath channels in the presence of the frequency offset.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.497-498
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• 2015

영상을 취득한 후 다양한 응용프로그램으로 확장이 가능한 4 차원 light field 영상은 일반적인 2 차원 공간 (spatial) 영역과 추가적인 2 차원 각 (angular) 영역으로 구성된다. 그러나 이러한 4 차원 light field 영상을 2 차원 CMOS 센서로 취득하므로 이에 따른 해상도 제약이 존재한다. 본 논문에서는 이러한 4 차원 light field 영상이 가지는 해상도 제약 조건을 해결하기 위하여, 4 차원 light field 영상에 적합한 학습 기반 (learning-based) 초해상도 (superresolution) 알고리즘을 제안한다. 제안하는 알고리즘은 공간영역 해상도 그리고 각영역의 해상도를 각각 2 배 향상시킨다. 실험에 사용되는 영상은 상용 light field 카메라인 Lytro 에서 취득하며, 기존의 선형 보간 기법인 bicubic 기법과의 비교를 통해 제안하는 기법의 우수성을 검증한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_2
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• pp.653-663
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• 2012

Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.

• Korean Journal of Optics and Photonics
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• v.7 no.2
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• pp.89-95
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• 1996

Superresolution optics, employing Gaussian apodization, is rigorously treated at the soft X-ray wave-length(λ=0.013 ${\mu}{\textrm}{m}$) region. In the diffraction integral, the line integral along the imaginary axis is found small, and it is ignored, so that the diffraction integral consists of the integration along the real axis. The resolution of the diffracted image is not effected by the pupil angular frequency bandwidth $2{\omega}_0$, which is one of the most important the characteristic features of Gaussian apodization ($e^{-o^2x^2}$ optics. The superresolution optics has resolution ($\frac{1}{2}{\times}FWHM)$=$\Delta$x=0.008 $\mu$m which is smaller than the Rayleigh criterion of 2λ=0.026 ${\mu}{\textrm}{m}$ for NA=0.25. The optical system has ${\omega}_0{\ge}\frac{1}{2}{\sigma}$, which gives the peak intensity of the diffracted image larger than $e^{-2}$ times intensity obtainable by the infinite sperture.