• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.17 no.2
• /
• pp.167-176
• /
• 1999

The study about quantitative or qualitative analysis of object using digital image is being progressed actively with the development of the image medium and image process technique. But, it is very high that the dependency about image acquisition system of high resolution for image analysis of high accuracy and it is a equipment of high-price. In this study, I extracted the optimum condition of image enhancement by analyzing and enhancing the multiple images which were acquired by system of low-price. And I carried out the analysis of 3D accuracy by being applied the optimum condition of image enhancement. In the result of analysis of average 3D positioning error using law image and enhanced image which is acquired by applying the optimum condition of image enhancement, I could obtain the progressed accuracy about 10％ on the enhanced image.

• ETRI Journal
• /
• v.32 no.3
• /
• pp.390-394
• /
• 2010

In this paper, we propose a new super-resolution technique based on interpolation of the high-frequency subband images obtained by discrete wavelet transform (DWT) and the input image. The proposed technique uses DWT to decompose an image into different subband images. Then the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new super-resolved image by using inverse DWT. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques. For Lena's image, the PSNR is 7.93 dB higher than the bicubic interpolation.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.4
• /
• pp.137-143
• /
• 2013

The high resolution satellite images are used in many fields such as weather observation, remote sensing, military facilities monitoring, cultural properties protection etc. Although satellite images are obtained in same satellite imaging system, the satellite images are degraded depending on the condition of hardware(optical device, satellite operation altitude, image sensor, etc.). Due to the fact that changing the hardware of satellite imaging system is impossible for resolution enhancement of these degraded satellite after launching a satellite, therefore the method of resolution enhancement with satellite images is necessary. In this paper the resolution is enhances by using a Super Resolution(SR) algorithm. The SR algorithm is an algorithm to enhance the resolution of an image by uniting many low resolution images, so an output image has higher resolution than using other interpolation methods. But It is difficult to obtain many images of the same area. Therefore, to solve this problem, we applied SR after by applying the affine and projection transform. As a results, we found that the images applied SR after affine and projection transform have higher resolution than the images only applied SR.

• Spatial Information Research
• /
• v.13 no.4 s.35
• /
• pp.345-354
• /
• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method has proven to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper, we applied the super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and are overlapped at high rate. We constructed the observation model between the HR images and LR images applied with the Maximum A Posteriori(MAP) reconstruction method which is one of the major methods in the super resolution grid construction. Based on the MAP method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.105-108
• /
• 1996

In this paper, we present an interpolation schema for image resolution enhancement using fuzzy logic. Proposed algorithm can recover both low and high frequency information in image data. In general, interpolation techniques are based on linear operators which are essentially details in the original image. In our fuzzy approach, the operator itself balances the strength of its sharpening and noise suppressing components according to the properties of the input image data. The proposed interpolation algorithm is performed in three step. First logic reasoning is applied to coarsely interpret the high frequency information. These results are combined to obtain the optical output. Using our approach, resolution of the original image can be applied to various kind of image processing topics such as image enhancement, subpixel edge detection, and filtering.

• Journal of Korea Multimedia Society
• /
• v.16 no.10
• /
• pp.1180-1188
• /
• 2013

Interpolation or super-resolution is used in order to restore degradation of image quality that appears after various transform of image. The method on subjective or objective image resolution improvement having low computation complexity has been being researched in many different ways. In this paper, image enhancement method using improved self degradation restoration(ISDR) method is proposed. The proposed method uses ISDR to estimate pixel value of missed coordinate in the process of image scaling, and combines the estimated loss information and interpolated image to generate enhanced result image. The proposed method shows that PSNR increases by 1.8dB, and subjective image quality is superior to other compared methods. The proposed method can be applied as a basis technique in variety of applications which requires image scale transform.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.07a
• /
• pp.272-275
• /
• 2020

Light Field (LF) cameras capture both spatial and directional information of light rays. Current LF cameras have a problem of a low spatial resolution. There have been lots of existing works carried out to improve the resolution of LF images. In this paper, those existing works will be divided into two categories: hardware-based approaches and software-based approaches, and we will look into and compare several experiment results in order for LF spatial resolution enhancement. Finally, the direction for the future spatial resolution enhancement will be suggested.

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.178-185
• /
• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.18 no.3
• /
• pp.263-269
• /
• 2000

As the accuracy of digital photogrammetry is restricted by the resolution of image to be used, it is axiomatic that the resolution of image should be improved. As for the method to constitute hardware with CCD sensor that capacity was expanded or the method to acquire the image of high resolution by deciding the quantity of sub-pixel in advance through moving sensor, the price is expensive. This study tries to enhance the resolution of low resolution image by acquiring the image with the digital camera that the price is cheap and deciding shifts and rotations through matching multiple digital image by means of least square method. As the result of study, the resolution of digital image was improved greatly. So, not only the digital photogrammetry which has the competitive power of price economically is possible in the future but also the application is expected widely.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.2
• /
• pp.45-50
• /
• 2010

An image interpolation is a technique to use for enhancement of image resolution, it have two problems which are image quality degradation of the interpolated result image and high computation complexity. In this paper, to solve the problem, we propose an image interpolation algorithm using loss information estimation and implement the proposed method on portable device. From reduction image of obtained low resolution image, the proposed method can computes error to use image interpolated and estimate loss information by interpolation of the computed error. The estimated loss information is added to interpolated high resolution image with weight factor. We verified that the proposed method has improved FSNR as 2dB than conventional algorithms by experiments. Also, we implemented the proposed method on portable device and checked up real-time action. The proposed algorithm may be helpful for various application for image enlargement and reconstruction.