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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.


Papers
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Patent
16 Jun 2021
TL;DR: In this paper, a rolling shutter is used for high dynamic range (HDR) imaging, where the first image data is captured using a first image sensor and a first exposure time, and the second image data are captured using one or more second image sensors with shorter exposure times than the first exposure times.
Abstract: Methods and apparatus, including computer program products, for high dynamic range (HDR) imaging. First image data is captured using a first image sensor and a first exposure time, using a rolling shutter such that different lines within the first image data are captured at different first capture times. Two or more instances of second image data are captured using one or more second image sensors and one or more second exposure times that are shorter than the first exposure time. The two or more instances of second image data are captured using a rolling shutter, and overlap at least in part with the first image data. A line of the first image data has a corresponding line in each instance of second image data, and the corresponding lines in the different instances of second image data are captured at different second capture times. For a line in the first image data, the corresponding line from the instance of second image data whose second capture time is closest to the first capture time, is selected to be merged with the line in the first image data to generate a high dynamic range image.
Patent
22 Sep 2016
TL;DR: In this paper, a high dynamic range video processing method performs merging and tone mapping techniques after a Bayer filter mosaic technique is performed and then converts it to red green blue (RGB) at the end as opposed to converting into RGB at the beginning.
Abstract: A high dynamic range video processing method performs merging and tone mapping techniques after a Bayer filter mosaic technique is performed and then converts it to red green blue (RGB) at the end as opposed to converting into RGB at the beginning and then performing merging and tone mapping after. The HDR processing is performed on Bayer-mosaic images and no de-mosaicing and color space conversions are required. The merging procedure has two modes: full-reset merging and LDR-updated merging. The first mode, full-reset merging, creates an HDR frame once the system has all image frames captured. The second mode, LDR-updating merging, means that any new HDR frame is obtained by an updating of a previous HDR frame with a new LDR frame data.
Posted Content
TL;DR: In this article, the authors proposed HDRCloudSeg, an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion, which achieves very good results.
Abstract: Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg -- an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.
Journal ArticleDOI
TL;DR: In this article , the authors propose a transformer model for HDR image reconstruction, which consists of alignment, fusion, and reconstruction, and outperforms the state-of-the-art on several popular public datasets.
Abstract: Reconstructing a High Dynamic Range (HDR) image from several Low Dynamic Range (LDR) images with different exposures is a challenging task, especially in the presence of camera and object motion. Though existing models using convolutional neural networks (CNNs) have made great progress, challenges still exist, e.g., ghosting artifacts. Transformers, originating from the field of natural language processing, have shown success in computer vision tasks, due to their ability to address a large receptive field even within a single layer. In this paper, we propose a transformer model for HDR imaging. Our pipeline includes three steps: alignment, fusion, and reconstruction. The key component is the HDR transformer module. Through experiments and ablation studies, we demonstrate that our model outperforms the state-of-the-art by large margins on several popular public datasets.

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202333
202260
202129
202034
201937
201837