• Journal of The Korean Digital Architecture Interior Association
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• v.12 no.3
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• pp.81-89
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• 2012

Luminance is the most important quantity in lighting design and illuminating engineering. There are three methods for measuring luminance; using a conventional luminance meter, through the illuminance measurement and subsequent calculations and using digital imaging photometer. Recently, HDRI(High Dynamic Range Imaging) technique introduces a new method of capturing luminance values in a lighting environment. The radiance maps from HDRI are commonly used as visual environment maps for lighting analysis applications. For the HDRI, HDR software is needed to create HDR image. Currently, there is number of HDR software available. The purpose of this paper is to investigate whether a luminance map can be accurately captured by the various types of HDR software which include HDR Shop and Photoshop. To accomplish this goal a set of experiments was conducted. In order to assess the luminance values of the HDR image from HDR software, the values had to be compared to the ones obtained with conventional methods of luminance measurement.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.550-553
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• 2010

최근 영화, 광고 그리고 증강현실과 혼합현실 등 다양한 분야에서 실제 영상에 가상의 객체를 합성하는 기법이 자주 사용되고 있다. 보다 사실적인 합성 결과를 생성하기 위해서는 실제 배경영상의 광원정보를 그대로 적용해야 한다. 이러한 실 세계의 광원 정보를 이용하기 위해서는 HDR(High Dynamic Range) 영상을 생성해야 한다. 일반적으로 HDR 영상을 생성하기 위해서는 고가의 HDR 카메라를 사용하거나 LDR(Low Dynamic Range) 카메라를 사용하여 노출 시간을 달리한 일련의 LDR 영상을 촬영하여 이를 기반으로 HDR 영상을 생성해야 한다. 본 논문에서는 이러한 단점을 보완하기 위해 한 장의 LDR 환경 맵을 HDR 환경 맵으로 복원하는 방법에 대해 제안한다. 제안하는 방법을 통해 LDR 환경 맵을 HDR 환경 맵으로 복원할 수 있으며 결과에서 볼 수 있듯이 HDR 영상을 이용했을 때와 유사한 렌더링 결과를 생성할 수 있다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.111-120
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• 2015

HDR(High dynamic range) imaging is a technique to represent a dynamic range of real world. Exposure fusion is a method to obtain a pseudo-HDR image and it directly fuses multi-exposure images instead of generating the true-HDR image. However, it results ghost artifacts while fusing the multi-exposure images with moving objects. To solve this drawback, temporal consistency assessment is proposed to remove moving objects. Firstly, multi-level threshold bitmap and brightness bitmap are proposed. In addition, hue-angle constancy map between multi-exposure images is proposed for compensating a bitmap. Then, two bitmaps are combined as a temporal weight map. Spatial domain image quality assessment is used to generate a spatial weight map. Finally, two weight maps are applied at each multi-exposure image and combined to get the pseudo-HDR image. In experiments, the proposed method reduces ghost artifacts more than previous methods. The quantitative ghost-free evaluation of the proposed method is also less than others.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.31-42
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• 2021

We propose a deep learning-based method that can estimate an appropriate lighting of both indoor and outdoor images. The method consists of two networks: Crop-to-PanoLDR network and LDR-to-HDR network. The Crop-to-PanoLDR network predicts a low dynamic range (LDR) environment map from a single partially observed normal field of view image, and the LDR-to-HDR network transforms the predicted LDR image into a high dynamic range (HDR) environment map which includes the high intensity light information. The HDR environment map generated through this process is applied when rendering virtual objects in the given image. The direction of the estimated light along with ambient light illuminating the virtual object is examined to verify the effectiveness of the proposed method. For this, the results from our method are compared with those from the methods that consider either indoor images or outdoor images only. In addition, the effect of the loss function, which plays the role of classifying images into indoor or outdoor was tested and verified. Finally, a user test was conducted to compare the quality of the environment map created in this study with those created by existing research.

• Journal of the Korea Computer Graphics Society
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• v.18 no.2
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• pp.1-8
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• 2012

High dynamic range (HDR) images represent a far wider numerical range of exposures than common digital images. Thus it can accurately store intensity levels of light found in the specific scenes generated by light sources in the real world. Although a kind of professional HDR cameras which support fast accurate capturing has been developed, high costs prevent from employing those in general working environments. The common method to produce a HDR image with lower cost is to take a set of photos of the target scene with a range of exposures by general purpose cameras, and then to transform them into a HDR image by commercial softwares. However, the method needs complicate and accurate camera calibration processes. Furthermore, creating HDR environment maps which are used to produce high quality imaging contents includes delicate time-consuming manual processes. In this paper, we present an automatic HDR panorama environment map generating system which was constructed to make the complicated jobs of taking pictures easier. And we show that our system can be effectively applicable to photo-realistic compositing tasks which combine 3D graphic models with a 2D background scene using image-based lighting techniques.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.7
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• pp.26-33
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• 2014

The purpose of this study is to evaluate the accuracy of luminance maps generated from five types of HDRI builder(High Dynamic Ranging Image builder) which include Photosphere, Bracket, Picturenaut, Luminance HDR and Photoshop. To accomplish this goal a set of experiments was conducted. In order to assess the luminance values of the HDR image from HDR image builder, the values had to be compared to the ones obtained from imaging photometer. After comparing measured luminance data using imaging photometer with those retrieved from the HDR images, Photosphere error rate estimated at 3% below.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.317-319
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• 2012

본 논문에서는 아웃라이어 처리를 통한 강인한 HDR 영상 복원 방법을 제시한다. 기존의 방법들은 LDR 영상들에서 흔히 발생하는 긴 노출시간으로 인한 블러 현상이나 저노출/과노출로 인한 포화 픽셀(아웃라이어)을 고려하지 않았다. 본 논문이 제시하는 방법은 MAP(Maximum a priori)을 이용하여 블러 및 아웃라이어를 반영하여 HDR 영상 복원 문제를 정확히 모델링하고, 블러 추정 및 EM(Expectation-Maximization) 알고리즘 기반의 아웃라이어 추정을 통해 품질 저하가 없는 선명한 HDR 영상을 복원한다. 실험 결과를 통해 본 논문이 제시하는 방법이 블러 및 아웃라이어를 포함하는 LDR 영상들로부터 우수한 품질의 HDR 영상을 효과적으로 복원할 수 있음을 보이며, 최근에 개발된 방법들과 비교해서도 더 우수한 품질을 갖는 것을 볼 수 있다.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.369-382
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• 2018

We propose a single-shot high dynamic range (HDR) imaging algorithm using a deep convolutional neural network (CNN) for row-wise varying exposures in a single image. The proposed algorithm restores missing information resulting from under- and/or over-exposed pixels in an input image and reconstructs the raw radiance map. The main contribution of this work is the development of a loss function for the CNN employing the human visual system (HVS) properties. Then, the HDR image is obtained by applying a demosaicing algorithm. Experimental results demonstrate that the proposed algorithm provides higher-quality HDR images than conventional algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.774-777
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• 2005

In this paper, we propose performance improvement algorithm for high data rate wireless personal area network (HDR-WPAN) system using turbo code. Turbo code increase detection delay and computation according to iterate counts. However, turbo code has been shown to be very close th the Shanon limit, can be classified as a block-based error correction code. Turbo code has gain about E$_b$/N$_o$=5.8dB at 10$^{-4}$ in the multipath indoor channel. In the result, HDR-WPAN system adopted turbo code has reliable communication by low power.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1399-1408
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• 2012

The light source is an important visual component that empirically affects the color and illumination of graphic objects, and it is necessary to precisely store and appropriately employ the information of all light sources in the real world in order to obtain photo-realistic composition results. The information of real light sources can be accurately stored in HDR environment maps; however, it is impossible to create new environment maps corresponding to dynamic virtual light sources from a single HDR environment map captured under a fixed lighting situation. In this paper, we present a technique to dynamically generate well-matched information for arbitrarily selected virtual light sources using HDR environment maps created under predefined lighting position and orientation. Using the information obtained from light intensity and distribution analysis, our technique automatically generates HDR environment maps for virtual light sources via image interpolation. By applying the interpolated environment maps to an image-based lighting technique, we show that virtual light can create photo-realistically rendered images for graphic models.