• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.452-455
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• 2007

3D stereoscopic viewing of large scale imagery, such as aerial photography and satellite images, needs different parallaxes relative to the display scale. For example, when a viewer sees a stereoscopic image of aerial photography, the optimal parallax of its zoom-in image should be smaller than that of its zoom-out. Therefore, relative parallax adjustment according to the display scale is required. Merely adjusting the spacing between stereo images is not appropriate because the depths of the whole image are either exaggerated or reduced entirely. This paper focuses on the improving stereoscopic viewing with a single remote sensing image and a digital surface model (DSM). We present the parallax adjustment technique to maximize the 3D realistic effect and the visual comfort. For remote sensing data, DSM height value can be regarded as disparity. There are two possible kinds of methods to adjust the relative parallax with a single image performance. One is the DSM compression technique: the other is an adjustment of the distance between the original image and its stereo-mate. In our approach, we carried out a test to evaluate the optimal distance between a single remote sensing image and its stereo-mate, relative to the viewing scale. Several synthetic stereo-mates according to certain viewing scale were created using a parallel projection model and their anaglyphs were estimated visually. The occlusion of the synthetic stereo-mate was restored by the inpainting method using the fields of experts (FoE) model. With the experiments using QuickBird imagery, we could obtain stereoscopic images with optimized parallax at varied display scales.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.54-63
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• 2007

The purpose of this study is to present a new energy minimization method for image completion with hierarchical approach. The goal of image completion is to fill in missing part in a possibly large region of an image so that a visually plausible outcome is obtained. An exemplar-based Markov Random Field Modeling(MRF) is proposed in this paper. This model can deal with following problems; detection of global features, flexibility on environmental changes, reduction of computational cost, and generic extension to other related domains such as image inpainting. We use the Priority Belief Propagation(Priority-BP) which is a kind of Belief propagation(BP) algorithms for the optimization of MRF. We propose the hierarchical Priority-BP that reduces the number of nodes in MRF and to apply hierarchical propagation of messages for image completion. We show that our approach which uses hierarchical Priority-BP algorithm in image completion works well on a number of examples.

• Journal of IKEEE
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• v.20 no.1
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• pp.31-44
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• 2016

This paper presents a hole-filling algorithm based on extrapolating spatial-temporal background information used in view synthesis for free-viewpoint television. A new background codebook is constructed and updated in order to extract reliable temporal background information. In addition, an estimation of spatial local background values is conducted to discriminate an adaptive boundary between the background region and the foreground region as well as to update the information about the hole region. The holes then are filled by combining the spatial background information and the temporal background information. In addition, an exemplar-based inpainting technique is used to fill the rest of holes, in which a priority function using background-depth information is defined to determine the order in which the holes are filled. The experimental results demonstrated that the proposed algorithm outperformed the other comparative methods about average 0.3-0.6 dB, and that it synthesized satisfactory views regardless of video characteristics and type of hole region.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.1-9
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• 2018

In this paper, we propose a method to restore corrupted black and white facial images to color. Previous studies have shown that when coloring damaged black and white photographs, such as old ID photographs, the area around the damaged area is often incorrectly colored. To solve this problem, this paper proposes a method of restoring the damaged area of input photo first and then performing colorization based on the result. The proposed method consists of two steps: BEGAN (Boundary Equivalent Generative Adversarial Networks) model based restoration and CNN (Convolutional Neural Network) based coloring. Our method uses the BEGAN model, which enables a clearer and higher resolution image restoration than the existing methods using the DCGAN (Deep Convolutional Generative Adversarial Networks) model for image restoration, and performs colorization based on the restored black and white image. Finally, we confirmed that the experimental results of various types of facial images and masks can show realistic color restoration results in many cases compared with the previous studies.

• Smart Media Journal
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• v.3 no.4
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• pp.28-34
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• 2014

Recently the depth-image-based rendering (DIBR) method is usually used in 3D image application filed. Virtual view image is created by using a known view with associated depth map to make a virtual view point which did not taken by the camera. But, disocclusion area occur because the virtual view point is created using a depth image based image 3D warping. To remove those kind of disocclusion region, many hole filling methods are proposed until now. Constant color region searching, horizontal interpolation, horizontal extrapolation, and variational inpainting techniques are proposed as a hole filling methods. But when using those hole filling method some problem occurred. The different types of annoying artifacts are appear in texture region hole filling procedure. In this paper to solve those problem, the multi-directional extrapolation method is newly proposed for efficiency of expanded hole filling performance. The proposed method is efficient when performing hole filling which complex texture background region. Consideration of directionality for hole filling method use the hole neighbor texture pixel value when estimate the hole pixel value. We can check the proposed hole filling method can more efficiently fill the hole region which generated by virtual view synthesis result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.15-31
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• 2013

This paper proposes an intermediate image generation method for the viewer's view point by tracking the viewer's face, which is converted to a digital hologram. Its purpose is to increase the viewing angle of a digital hologram, which is gathering higher and higher interest these days. The method assumes that the image information for the leftmost and the rightmost view points within the viewing angle to be controlled are given. It uses a stereo-matching method between the leftmost and the rightmost depth images to obtain the pseudo-disparity increment per depth value. With this increment, the positional informations from both the leftmost view point and the rightmost view point are generated, which are blended to get the information at the wanted intermediate viewpoint. The occurrable dis-occlusion region in this case is defined and a inpainting method is proposed. The results from implementing and experimenting this method showed that the average image qualities of the generated depth and RGB image were 33.83[dB] and 29.5[dB], respectively, and the average execution time was 250[ms] per frame. Also, we propose a prototype system to service digital hologram interactively to the viewer by using the proposed intermediate view generation method. It includes the operations of data acquisition for the leftmost and the rightmost viewpoints, camera calibration and image rectification, intermediate view image generation, computer-generated hologram (CGH) generation, and reconstruction of the hologram image. This system is implemented in the LabView(R) environments, in which CGH generation and hologram image reconstruction are implemented with GPGPUs, while others are implemented in software. The implemented system showed the execution speed to process about 5 frames per second.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.