• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.1
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• pp.56-74
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• 2023

In order to obtain practical and high-quality satellite images containing high-frequency components, a large aperture optical system is required, which has a limitation in that it greatly increases the payload weight. As an attempt to overcome the problem, many multi-aperture optical systems have been proposed, but in many cases, these optical systems do not include high-frequency components in all directions, and making such an high-quality image is an ill-posed problem. In this paper, we use deep learning to overcome the limitation. A deep learning model receives low-quality images as input, estimates the Point Spread Function, PSF, and combines them to output a single high-quality image. We model images obtained from three rectangular apertures arranged in a regular polygon shape. We also propose the Modulation Transfer Function Loss, MTF Loss, which can capture the high-frequency components of the images. We present qualitative and quantitative results obtained through experiments.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.105-111
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• 2014

Image resolution enhancement is a technique to generate high-resolution image through improving resolution of low-resolution obtained image. It is important to estimate correctly missing pixel value in low-resolution obtained image for image resolution enhancement. In this paper, multiple shortfall estimation method for image resolution enhancement is proposed. The proposed method estimate separate multiple shortfall by predictive degradation-restoration processing in sub-images of obtained image, and generate result image combining the estimated shortfall and interpolated obtained-image. Lastly, final reconstruction image is generated by deblurring of the result image. The experimental results demonstrate that the proposed method has the best results of all compared methods in objective image quality index: PSNR, SSIM, and FSIM. The quality of reconstructed image is superior to all compared methods, and the proposed method has better lower computational complexity than compared methods. The proposed method can be useful for image resolution enhancement.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.7
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• pp.327-331
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• 2006

We have proposed an image restoration method for a bi-level waveforms whose number of edges is known to us. Based on the information, we parametrize a bi-level waveform using the locations of edges and restore the waveform by estimating the parameter. We estimated the locations by maximizing the correlation coefficients between the hi-level waveform and the measured waveform. In experiments using two dimensional barcode images of the PDF417 specification, the proposed method showed better performance than conventional methods in the sense that the proposed method was able to decode barcode images that were not decoded by the conventional methods.

• 전기의세계
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• v.31 no.3
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• pp.204-208
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• 1982

The objective of this paper is to present a digital method for improving the lateral resolution of the B-scan images in the medical applications of ultrasound. The method is based upon a mathematical model of the lateral blurring caused by the finite beam width of the transducers. This model provides a simple method of applying a recursive scheme for image restoration with fast computation time. The point spread function (P.S.F.) can be measured by the reflective signals after scanning the small pins located along the depth of interest. From the measured P.S.F., one can compute the coefficient matrices of the inverse discrete-time dynamic state variable equation of the blurring process. Then, a recursive scheme for deblurring is applied to the recorded B-scan to improve the lateral resolution. One major advantage of the present recursive scheme over the transform method is in its applicability for the space-variant imaging, such as in the case of the rotational movement of transducer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.165-175
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• 2005

If the interlaced scan type camera moves while an image is filming from the camera, blur is often created from the misalignment of the two images of even and odd lines. This paper proposed an algorithm which removes the misalignment of the even and odd line images cased by the vibration of the interlaced scan type camera. The blurred original image is separated into the even and the odd line images as half size. Based on these two images, two full sized images are generated using interpolation technique. If a big difference between these two interpolated images is generated, the original image is taken while the camera is moving. In this case, a deblurred image is obtained with the alignment of these separated two images through feature point extraction, feature point matching, sub-pixel matching, outlier detection, and image mosaicking processes. This paper demonstrated that the proposed algorithm can create clear images from blurred images caused by various camera motions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.83-90
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• 2023

Blurred image is an important factor in lowering image recognition rates in Computer vision. This mainly occurs when the camera is unstablely out of focus or the object in the scene moves quickly during the exposure time. Blurred images greatly degrade visual quality, weakening visibility, and this phenomenon occurs frequently despite the continuous development digital camera technology. In this paper, it replace the modified building module based on the Deep multi-patch neural network designed with convolution neural networks to capture details of input images and Attention techniques to focus on objects in blurred images in many ways and strengthen the image. It measures and assigns each weight at different scales to differentiate the blurring of change and restores from rough to fine levels of the image to adjust both global and local region sequentially. Through this method, it show excellent results that recover degraded image quality, extract efficient object detection and features, and complement color constancy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4604-4622
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• 2015

It is not easy to reconstruct the geometrical characteristics of the distorted images captured by the devices. One of the most popular optimization methods is fast iterative shrinkage/ thresholding algorithm. In this paper, to deal with its approximation error and the turbulence of the decrease process, an adaptive proximal conjugate gradient (APCG) framework is proposed. It contains three stages. At first stage, a series of adaptive penalty matrices are generated iterate-to-iterate. Second, to trade off the reconstruction accuracy and the computational complexity of the resulting sub-problem, a practical solution is presented, which is characterized by solving the variable ellipsoidal-norm based sub-problem through exploiting the structure of the problem. Third, a correction step is introduced to improve the estimated accuracy. The numerical experiments of the proposed algorithm, in comparison to the favorable state-of-the-art methods, demonstrate the advantages of the proposed method and its potential.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.551-566
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• 2016

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. Especially, the degradation gives bad influence in the analysis of images collected over the scene with complicate surface structure such as urban area. This study proposes a multi-stage image restoration to improve the accuracy of detailed analysis for the images collected over the complicate scene. The proposed method assumes a Gaussian additive noise, Markov random field of spatial continuity, and blurring proportional to the distance between the pixels. Point-Jacobian Iteration Maximum A Posteriori (PJI-MAP) estimation is employed to restore a degraded image. The multi-stage process includes the image segmentation performing region merging after pixel-linking. A dissimilarity coefficient combining homogeneity and contrast is proposed for image segmentation. In this study, the proposed method was quantitatively evaluated using simulation data and was also applied to the two panchromatic images of super-high resolution: Dubaisat-2 data of 1m resolution from LA, USA and KOMPSAT3 data of 0.7 m resolution from Daejeon in the Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution panchromatic imagery.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.200-209
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• 2016

This paper proposes a non-uniform de-blur algorithm using IMU sensor and a long/short exposure-time image pair to efficiently remove the blur phenomenon. Conventional blur kernel estimation algorithms using sensor information do not provide acceptable performance due to limitation of sensor performance. In order to overcome such a limitation, we present a kernel refinement step based on images having different exposure times which improves accuracy of the estimated kernel. Also, in order to figure out the phenomenon that conventional non-uniform de-blur algorithms suffer from severe degradation of visual quality in case of large blur kernels, this paper a homography-based residual de-convolution which can minimize quality degradation such as ringing artifacts during de-convolution. Experimental results show that the proposed algorithm is superior to the state-of-the-art methods in terms of subjective as well as objective visual quality.