Topic
High-dynamic-range imaging
About: High-dynamic-range imaging is a research topic. Over the lifetime, 766 publications have been published within this topic receiving 22577 citations.
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01 Jun 2010
TL;DR: This paper proposes a solution to generate a high dynamic range (HDR) image on a digital still camera, considering a minimum resource and a variable dynamic range capturing, and is implemented onto a commercialized camera system.
Abstract: Film photographers had used to "dodge and burn" to express a greater dynamic range than original photographic paper. The greater dynamic range imaging is a big challenge on general digital camera area. In this paper, we propose a solution to generate a high dynamic range (HDR) image on a digital still camera, considering a minimum resource and a variable dynamic range capturing. Our approach consists of three parts: scene capturing, HDR image generation and DR compression. The scene capturing needs an adaptive controlling of the exposure time during multiple captures. The HDR image generation from multiple captured images has to preserve and combine the captured dynamic range data for storage and display having low dynamic range (LDR). Our research is implemented onto a commercialized camera system and is evaluated by the maximum capturing dynamic range and the HDR image quality
2 citations
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21 Jun 2011TL;DR: In this article, a weighted frame averaging method based on an intensity mapping function (IMF) is proposed to reduce noise from the input low dynamic range (LDR) images with shorter exposures.
Abstract: A high quality image can be synthesized by combining several differently exposed low dynamic range (LDR) images of the same scene. For scenes under low lighting condition, cameras are usually set to high sensitivity mode to reduce exposure times and avoid motion blur on captured images. However, those images tend to be noisy and the noise severely degrades the visual quality of final image especially on dark areas. In this paper, a weighted frame averaging method based on an intensity mapping function (IMF) is proposed to reduce noise from the input LDR images with shorter exposures. The proposed method does not require any knowledge on either camera response functions (CRFs) or exposure times, and it is much simpler than the state-of-art method. Experiment results also show that the proposed method effectively removes the noise from the shortly exposed LDR images without introducing any blurring or other artifacts.
2 citations
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01 Dec 2011
TL;DR: Two workflows that have been developed using inexpensive digital cameras and open source software, supporting the interactive exploration of realistic lighting conditions, both qualitatively and quantitatively, in a digital environment are discussed.
Abstract: High Dynamic Range (HDR) imaging is a popular photographic technique that accurately captures the lighting conditions of the scene from which they are created. HDR images can subsequently be used to illuminate 3D virtual scenes realistically, or be a source for numerical lighting simulations. This paper discusses two workflows that have been developed using inexpensive digital cameras and open source software, supporting the interactive exploration of realistic lighting conditions, both qualitatively and quantitatively, in a digital environment. The paper first details the suitability of the qualitative workflow as a vehicle for teaching the technical aspects of lighting to architecture students, due to its relative ease of implementation, close relationship to existing design tools, and application to visual design work. Finally, the extension of this technique to enable simplified numerical analysis is discussed.
2 citations
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01 Dec 2016TL;DR: This paper proposes a non-local mapping based on learned features directly from the image-texture, using a Convolutional Neural Network, which yields sub-optimal results and is demonstrated using various applications in the HDR domain.
Abstract: Color mapping is a fundamental task for many important computer vision applications such as High Dynamic Range Imaging (HDRI), Stereo Matching, Camera Calibration and various other tasks. Typically, the task of color mapping is to transfer the colors of an image to a reference distribution. For example, this way, it is possible to simulate different camera exposures using a single image, e.g., by transforming a dark image to a brighter image showing the same scene. Most approaches for color mapping are local in the sense that they just apply a pixel-wise (local) mapping to generate the color mapped image. In this paper, we empirically show that this approach yields sub-optimal results and we propose a non-local mapping based on learned features directly from the image-texture, using a Convolutional Neural Network. This way, we learn to generate an image which would have been captured by a certain factor of the actual exposure time. We demonstrate our method using various applications in the HDR domain and compare our results against other state-of-the-art methods where we obtain excellent results, both visually as well as numerically.
2 citations
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13 Nov 2014TL;DR: This work proposes a novel general framework for spherical HDR imaging for image-based virtual environments from a moving camera composed of three major stages: calibration and alignment, spherical stereo matching and HDR composition.
Abstract: Most high dynamic range (HDR) imaging techniques generate HDR radiance maps from exposure bracketed low dynamic range (LDR) images captured with a stationary camera. We propose a novel general framework for spherical HDR imaging for image-based virtual environments from a moving camera. The framework is composed of three major stages: calibration and alignment, spherical stereo matching and HDR composition. In the first stage, camera poses are found and spherical images are rotationally aligned. In the second stage, disparity maps are calculated with a spherical stereo vision toolkit. In the third stage, spherical images are warped from neighboring views to a target view based on enhanced disparity maps, and a spherical HDR radiance map is obtained from the warped exposure bracket. Our method is efficient because we generate a spherical HDR image for each of the viewpoints of the LDR images. We demonstrate our framework on indoor and outdoor scenes and compare our results with two recent state-of-the-art HDR imaging methods.
2 citations