• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3745-3761
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• 2020

Low-rank representation methods already achieve many applications in the image reconstruction. However, for high-gradient image patches with rich texture details and strong edge information, it is difficult to find sufficient similar patches. Existing low-rank representation methods usually destroy image critical details and fail to preserve edge structure. In order to promote the performance, a new representation-based image super-resolution reconstruction method is proposed, which combines gradient domain guided image filter with the structure-constrained low-rank representation so as to enhance image details as well as reveal the intrinsic structure of an input image. Firstly, we extract the gradient domain guided filter of each atom in high resolution dictionary in order to acquire high-frequency prior information. Secondly, this prior information is taken as a structure constraint and introduced into the low-rank representation framework to develop a new model so as to maintain the edges of reconstructed image. Thirdly, the approximate optimal solution of the model is solved through alternating direction method of multipliers. After that, experiments are performed and results show that the proposed algorithm has higher performances than conventional state-of-the-art algorithms in both quantitative and qualitative aspects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.355-362
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• 2009

A new intra field deinterlacing algorithm with edge map in the image block is introduced. Conventional deinterlacing methods usually employ edge-based line average algorithm within pixel-by-pixel approach. However, it is sensitive to variation of intensity. To reduce this shortcoming, we proposed edge direction vector computed by edge map, and also its interpolation technique. We first introduce an edge direction vector, which is computed by Sobel mask, so that finer resolution of the edge direction can be acquired. The proposed edge direction vector oriented deinterlacer operates by identifying small pixel variations in five orientations, while weighted averaging to estimate missing pixel. According to the edge direction of the direction vector, we calculate weights on each edge direction. These weight values multiplied by the candidate deinterlaced pixels in order to successively build approximations of the deinterlaced sequence.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.145-156
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• 2003

This paper for real time object tracking in this treatise detect histogram analysis that is accumulation value of binary conversion density and edge information and body that move by real time use of difference Image techniques and proposed method to object tracking. Firstly, we extract edge that can reduce quantity of data keeping information about form of input image in object detection. Object is extracted by performing difference image and binarization in edge image. Area of detected object is determined by threshold value that divide sum of horizontal accumulation value about binary conversion density by value that add horizontalityㆍverticality maximum accumulation value. Object is tracked by comparing similarity with object that is detected in previous frame and present frame. As experiment result, proposed algorithm could improve the object detection speed, and could track object by real time and could track local movement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2345-2354
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• 1998

There have been many researches to yield higher resolution image quality from the low resolution CCD device. The resolution of it is primary factor for the image quality of digital still camera and in manufacturing price. IN this paper, image enlargement algorithm, which reduces blocking effect of enlarged low resolution image and minimizes ringing and blur effect occurring around edge in linear interpolation, is proposed. This algorithm is composed of gaussian low pass filter which eliminates aliasing, least square spline interpolation and non-linear interpolation based on step edge model.

• Journal of IKEEE
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• v.21 no.1
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• pp.46-65
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• 2017

This paper proposes a fast patch-based de-blurring algorithm including kernel estimation based on the angle between the edge and the blur direction. For de-blurring, image patches from the most informative edges in the blurry image are used to estimate a kernel with low computational cost. Moreover, the kernels of each patch are estimated based on the correlation between the edge direction and the blur direction. This makes the final kernel more reliable and creates an accurate latent image from the blurry image. The combination of directionally oriented kernel estimation and patch-based de-blurring is faster and more accurate than existing state-of-the art methods. Experimental results using various test images show that the proposed method achieves its objectives: speed and accuracy.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.411-420
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• 1999

In geology, lineament features have been used to identify geological events, and many of scientists have been developed the algorithm that can be applied with the computer to recognize the lineaments. We choose several edge detection filter, line detection filters and Hough transform to detect an edge, line, and to vectorize the extracted lineament features, respectively. firstly the edge detection filter using a first-order derivative is applied to the original image In this step, rough lineament image is created Secondly, line detection filter is used to refine the previous image for further processing, where the wrong detected lines are, to some extents, excluded by using the variance of the pixel values that is composed of each line Thirdly, the thinning process is carried out to control the thickness of the line. At last, we use the Hough transform to convert the raster image to the vector one. A Landsat image is selected to extract lineament features. The result shows the lineament well regardless of directions. However, the degree of extraction of linear feature depends on the values of parameters and patterns of filters, therefore the development of new filter and the reduction of the number of parameter are required for the further study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2464-2478
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• 2013

Image stitching techniques produce an image with a wide field-of-view by aligning multiple images with a narrow field-of-view. While conventional algorithms successfully stitch images with a small parallax, structure misalignment may occur when input images contain a large parallax. This paper presents an image stitching algorithm that aligns images with a large parallax by regional linear warping. To this end, input images are first approximated as multiple planar surfaces, and different linear warping is applied to each planar surface. For approximating input images as multiple planar surfaces, the concept of dominant edges is introduced. Dominant edges are defined as conspicuous edges of lines in input images, and extracted dominant edges identify the boundaries of each planar surface. Dominant edge extraction is conducted by detecting distinct changes of local characteristics around strong edge pixels. Experimental results show that the proposed algorithm successfully stitches images with a large parallax without structure misalignment.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.243-252
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• 2005

Wavelet transform and its inverse processing provide the effective framework for data fusion. The purpose of this study is to investigate applicability of wavelet transform using texture images for the urban remote sensing application. We tried several experiments regarding image fusion by wavelet transform and texture imaging using high resolution images such as IKONOS and KOMPSAT EOC. As for texture images, we used homogeneity and ASM (Angular Second Moment) images according that these two types of texture images reveal detailed information of complex features of urban environment well. To find out the useful combination scheme for further applications, we performed DWT(Discrete Wavelet Transform) and IDWT(Inverse Discrete Wavelet Transform) using texture images and original images, with adding edge information on the fused images to display texture-wavelet information within edge boundaries. The edge images were obtained by the LoG (Laplacian of Gaussian) processing of original image. As the qualitative result by the visual interpretation of these experiments, the resultant image by each fusion scheme will be utilized to extract unique details of surface characterization on urban features around edge boundaries.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.947-959
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• 2021

Aiming at the problem of low accuracy in the water boundary automatic extraction of islands from GF-2 remote sensing image with high resolution in three bands, new water edges automatic extraction method in island based on GF-2 remote sensing images, genetic algorithm (GA) method, is proposed in this paper. Firstly, the GA-OTSU threshold segmentation algorithm based on the combination of GA and the maximal inter-class variance method (OTSU) was used to segment the island in GF-2 remote sensing image after pre-processing. Then, the morphological closed operation was used to fill in the holes in the segmented binary image, and the boundary was extracted by the Sobel edge detection operator to obtain the water edge. The experimental results showed that the proposed method was better than the contrast methods in both the segmentation performance and the accuracy of water boundary extraction in island from GF-2 remote sensing images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1700-1721
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• 2017

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.