• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1995년도 학술대회
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• pp.91-96
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• 1995

In this paper we propose algorithms for high resolution image sequence interpolation. Image sequences, in general, are assumed to have greater amount of information than a still image. By this reason, image sequences can be used to improve the resolution of interpolated image sequences. Therefore the proposed algorithms can be the theoretical basis for interpolating dynamic image sequences. In order to demonstrate the validity of the proposed algorithms, experimental results using both synthetic and real test images are presented.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.299-301
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• 1996

Since a standard lens has small sight angle, a fish-eye lens can be used in order to obtain wide sight angle for the robot vision system. In spite of the advantage, the image through the lens has variable resolution; the central information of the lens is of high resolution, but the peripheral information is of low resolution. Owing to this difference of resolution, the variable resolution image should be transformed to a uniform resolution image in order to determine the positions of the objects in the image. In this work, the correction method for the distorted image is presented and the performance is analyzed. Furthermore, the camera with a fish eye lens can be used to determine the real world coordinates. The performance is shown through experiments.

• Smart Structures and Systems
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• 제30권3호
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Current Optics and Photonics
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• 제8권3호
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• pp.270-281
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• 2024

Information-hiding technology is introduced into an optical ghost imaging encryption scheme, which can greatly improve the security of the encryption scheme. However, in the current mainstream research on camouflage ghost imaging encryption, information hiding techniques such as digital watermarking can only hide 1/4 resolution information of a cover image, and most secret images are simple binary images. In this paper, we propose an equal-resolution image-hiding encryption scheme based on deep learning and computational ghost imaging. With the equal-resolution image steganography network based on deep learning (ERIS-Net), we can realize the hiding and extraction of equal-resolution natural images and increase the amount of encrypted information from 25% to 100% when transmitting the same size of secret data. To the best of our knowledge, this paper combines image steganography based on deep learning with optical ghost imaging encryption method for the first time. With deep learning experiments and simulation, the feasibility, security, robustness, and high encryption capacity of this scheme are verified, and a new idea for optical ghost imaging encryption is proposed.

• International Journal of Computer Science & Network Security
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• 제21권12spc호
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• pp.463-468
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• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.

• 대한원격탐사학회지
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• 제25권6호
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• pp.517-529
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• 2009

Many satellite image fusion techniques have been developed in order to produce a high resolution multispectral (MS) image by combining a high resolution panchromatic (PAN) image and a low resolution MS image. Heretofore, most high resolution image fusion techniques have used IKONOS and QuickBird images. Recently, GeoEye-1, offering the highest resolution of any commercial imaging system, was launched. In this study, we have experimented with GeoEye-1 images in order to evaluate which fusion algorithms are suitable for these images. This paper presents compares and evaluates the efficiency of five image fusion techniques, the $\grave{a}$ trous algorithm based additive wavelet transformation (AWT) fusion techniques, the Principal Component analysis (PCA) fusion technique, Gram-Schmidt (GS) spectral sharpening, Pansharp, and the Smoothing Filter based Intensity Modulation (SFIM) fusion technique, for the fusion of a GeoEye-1 image. The results of the experiment show that the AWT fusion techniques maintain more spatial detail of the PAN image and spectral information of the MS image than other image fusion techniques. Also, the Pansharp technique maintains information of the original PAN and MS images as well as the AWT fusion technique.

• 대한원격탐사학회지
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• 제23권6호
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• pp.547-552
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• 2007

본 연구에서는 고해상도의 panchromatic 영상을 이용하여 저해상도의 multispectral 영상을 고해상도로 재구축하는 방법을 제시하고 있다. 제안된 방법은 저해상도와 고해상도 간의 선형 모형 사용하여 실제의 spectral 값에 부합하는 고해상도 영상을 재구축하며 두 단계로 이루어 진다. 첫 단계는 고해상도 feature와 연관된 저해상도의 선형 모형을 이용하여 최소 자승 오류 법에 의한 global 추정 과정이고 두 번째 단계는 재구축된 영상을 지역적으로 원래의 spectral 값과 일관되게 만드는 local 수정 과정이다. 본 연구에서 제안 방법을 이용하여 6m KOMPSAT-1 EOC 자료와 30m LANDSAT ETM+에 적용하였고 또한 IKONOS 1m RGB 영상 생성하였다. 실험 결과는 새로이 제시된 방법이 저해상도 Multispectral 영상의 고해상도 재구축에 탁월한 성능을 가지고 있음을 보여주었다.

• 대한전자공학회논문지SP
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• 제45권6호
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• pp.117-124
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• 2008

이미지 센서의 물리적 한계 가운데 공간 해상도의 제약과 다이내믹 레인지의 제약을 극복하기 위한 방법 가운데 신호처리기법에 기반하여 여러 장의 저해상도 영상으로부터 고해상도 영상을 복원하는 것과, 다이내믹 레인지가 좁은 여러 장의 영상으로부터 넓은 다이내믹 레인지를 갖는 영상을 복원하는 방법이 있다. 하지만, 일반적으로 실제 영상을 획득하는 과정에서 공간 해상도와 다이내믹 레인지의 제약을 동시에 받게 되므로, 이 두 제약을 동시에 극복하는 연구가 필요하다. 본 논문에서는 영상 장치의 응답 함수의 추정과 함께 공간 해상도와 다이내믹 레이지를 동시에 향상시킬 수 있는 알고리즘을 제안한다. 이를 위해 영상의 공간 해상도 제한과, 다이내믹 레인지의 제약을 포함하는 영상 획득 과정을 모델링하고, 이 영상 획득 모델을 기반으로 하여 영상 입력 장치의 응답 함수를 추정하고, 영상의 공간 해상도와 다이내믹 레인지를 동시에 향상시킬 수 있는 알고리즘을 제안한다. 실험 결과를 통해 제안된 알고리즘이 기존의 고해상도 복읜 알고리즘과 와이드 다이내믹 레인지 영상 복원을 연속적으로 처리한 결과보다 시각적, 수치적으로 더 좋은 결과를 보여줌을 확인할 수 있다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.18-21
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• 2008

Lee (2008) proposed the pansharpening method to reconstruct at the higher resolution the multispectral images which agree with the spectral values observed from the sensor of the lower resolution values. It outperformed over several current techniques for the statistical analysis with quantitative measures, and generated the imagery of good quality for visual interpretation. However, if a small object stretches over two adjacent pixels with different spectral characteristics at the lower resolution, the pixels of the object at the higher resolution may have different multispectral values according to their location even though they have a same intensity in the panchromatic image of higher resolution. To correct this problem, this study employed an iterative technique similar to the image restoration scheme of Point-Jacobian iterative MAP estimation. The effect of pansharpening on image segmentation/classification was assessed for various techniques. The method was applied to the IKONOS image acquired over the area around Anyang City of Korea.

• 대한전자공학회논문지SP
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• 제38권2호
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• pp.169-178
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• 2001

고해상도 영상에 대한 요구는 점점 더 증가되고 있지만, 기존의 영상 시스템들이 어느 정도의 엘리어싱을 발생하기 때문에 해상도의 저하가 생긴다. 따라서 엘리어싱이 발생한 다수의 저해상도 영상들을 사용하여 하나의 고해상도 영상을 재구성해 내는 디지털 영상 처리기법이 연구되어 왔다. 기존의 연구들은 저해상도 영상들간의 부화소 단위의 움직임 정보가 정확하다고 가정하였기 때문에, 움직임 정보가 정확하지 않은 경우에 대해서는 만족할 만한 고해상도 영상을 얻을 수 없었다. 따라서 본 논문에서는 부화소 단위의 움직임 정보가 정확하지 않기 때문에 생긴 고해상도 영상 내의 왜곡을 줄일 수 있는 알고리즘을 제안하고자 한다. 이를 위해 정확하지 않은 부화소 단위의 움직임 정보가 고해상도 영상 재구성에 미치는 영향을 분석하고, 이를 각각의 저해상도 영상에 대하여 가우시안 오류가 첨가된 것으로 모델링하였다. 그리고 고해상도 영상 내의 왜곡을 줄이기 위해서 다중채널 디컨벌루션 기법을 수정하여 적용하였다. 본 논문에서 제안된 알고리즘의 타당성은 이론 및 실험을 통하여 검증할 수 있었다.