• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.231-239
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• 2002

This paper presents a new fractal coding scheme to find sub-optimal transformation by performing an iterative encoding process. An optimal transformation can be defined as the transformation generating the attractor which is closest to an original image. Unfortunately, it has been well-known that it is actually impossible to find the optimal transformation due to heavy computation. In this paper, however, by means of some new theorems related with the fractal transformation due the attractor, it is shown that for a special case the optimal transformation can be obtained as well as for a general case the sub-optimal transformation. The proposed method based on the theorems obtains the sub-optimal transformation performing an iterative process as if done in decoding. Thus, it requires more computation than the conventional method but improves the image quality. We verify the superiority of the proposed method through the experimental results fur real images, which shows that the proposed method approaches to the optimal method in the performance and is superior to the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.226-230
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• 2002

This paper proposes a method of improvement of accuracy of projective transformation matrix for Image Mosaicing. Using shift theorem, we extracted global translation components between images and using translation components, we found matching points between images so we solve general matching point problem we extracted highly trusted matching point using RANSAC algorithm. we normalized matching point coordinates and improved accuracy of projective transformation matrix.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.4
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• pp.14-23
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• 2005

This paper proposes a method for matching two different images, especially satellite images. In the general image matching fields, when an image is compared to other image, they may have different properties on the size, contents, brightness, etc. If there is no noise in each image, in other words, they have identical pixel level and unchanged edges, the image matching method will be simple comparison between two images with pixel by pixel. However, in many applications, most of images to be matched should have much different properties. This paper proposes an efficient method for matching satellite images. This method is to match a raw satellite image with GCP chips. From this we can make a geometrically corrected image. The proposed method is based on wavelet transformation, not required any pre-processing such as histogram equalization, analysis of raw image like the traditional methods.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.49-59
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• 2018

Encryption of digital images has been requested various fields. In the meantime, many algorithms based on a text - based encryption algorithm have been proposed. In this paper, we propose a method of encryption in wavelet transform domain to utilize the characteristics of digital image. In particular, wavelet transform is used to reduce the association between the encrypted image and the original image. Wavelet packet transformations can be decomposed into more subband images than wavelet transform, and various position permutation, numerical transformation, and visual transformation are performed on the coefficients of this subband image. As a result, this paper proposes a method that satisfies the characteristics of high encryption strength than the conventional wavelet transform and reversibility. This method also satisfies the lossless symmetric key encryption and decryption algorithm. The performance of the proposed method is confirmed by visual and quantitative. Experimental results show that the visually encrypted image is seen as a completely different signal from the original image. We also confirmed that the proposed method shows lower values of cross correlation than conventional wavelet transform. And PSNR has a sufficiently high value in terms of decoding performance of the proposed method. In this paper, we also proposed that the degree of correlation of the encrypted image can be controlled by adjusting the number of wavelet transform steps according to the characteristics of the image.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.295-302
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• 2022

Sparse-view computed tomography (CT) imaging technique is able to reduce radiation dose, ensure the uniformity of image characteristics among projections and suppress noise. However, the reconstructed images obtained by the sparse-view CT imaging technique suffer from severe artifacts, resulting in the distortion of image quality and internal structures. In this study, we proposed a convolutional neural network (CNN) with wavelet transformation and residual learning for reducing artifacts in sparse-view CT image, and the performance of the trained model was quantitatively analyzed. The CNN consisted of wavelet transformation, convolutional and inverse wavelet transformation layers, and input and output images were configured as sparse-view CT images and residual images, respectively. For training the CNN, the loss function was calculated by using mean squared error (MSE), and the Adam function was used as an optimizer. Result images were obtained by subtracting the residual images, which were predicted by the trained model, from sparse-view CT images. The quantitative accuracy of the result images were measured in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The results showed that the trained model is able to improve the spatial resolution of the result images as well as reduce artifacts in sparse-view CT images effectively. Also, the trained model increased the PSNR and SSIM by 8.18% and 19.71% in comparison to the imaging model trained without wavelet transformation and residual learning, respectively. Therefore, the imaging model proposed in this study can restore the image quality of sparse-view CT image by reducing artifacts, improving spatial resolution and quantitative accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.2 s.20
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• pp.49-56
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• 2002

Many users of 3D virtual city are Utilize a texture image for the cognition of real object. In this study, building's facet images were achieved by a digital camera and adjusted its distortion by use of the 2D projective transformation method. After then, Images are mapped to a 3D building model by means of the OpenGL. Application program is able to offer an automation solution to construction process of the 3D virtual city.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1085-1088
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• 1999

This paper presents a method for soccer scene analysis and coordinate transformation from scene to ground model using a priori knowledge. First, the ground and spectator regions are separated, and various objects are extracted from the separated ground region. Second, an affine model is used for mapping the object positions on the soccer image into the position on the ground model. Problems regarding holes arising from mapping processing are solved using inverse mapping instead of a usual interpolation method. Experiments are performed on a PC using about 100 RGB images acquired at 240＊640 resolution and 3∼5 frames per second.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.1-7
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• 2011

In the bomb impact test, to acquire the bomb impact point location the high-priced embedded equipments such as the Bomb Scoring System or the EOTS are needed. Recently, a high-resolution image processing could be possible since the resolution of the commercial camera is growing rapidly. In this paper we first propose an image transformation method for acquiring the real bomb impact image using a high-resolution commercial camera, and then present the process calculating the real bomb impact point location coordinate from the transformed image. Based on the experimental results we found the possibilities that the real bomb impact point information could be effectively earned just using the commercial camera.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.1 no.2
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• pp.23-29
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• 1983

Degradation of image caused by relative motion between the object and the imaging system (like a camera with its platform) is detrimental to precision photogrammetry. Principal modes of relative motion are identified. The discussion is, however, concentrated on the systematic motions, translatory and rotatory. Various analogical approaches of compensating for the image motion are cited. An analytical-computational approach is presented. This one considers the relationship of transformation bet ween the image and the object, known as the collinearity condition. The standard forms of collinearity condition equations are presented. Augmentation of these equations with regard to both translatory and rotatory motions are expounded. With ever increasing use of high speed computers (as well as analytical plotters in the realm of photogrammetry), this approach seems to be more costeffective and seems to yield better precision in the long run than other approaches that concentrate on analogical corrections to the image itself.