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.

High-Dynamic-Range Spectral Imaging System for Omnidirectional Scene Capture

Abstract: Omnidirectional imaging technology has been widely used for scene archiving. It has been a crucial technology in many fields including computer vision, image analysis and virtual reality. It should be noted that the dynamic range of luminance values in a natural scene is quite large, and the scenes containing various objects and light sources consist of various spectral power distributions. Therefore, this paper proposes a system for acquiring high dynamic range (HDR) spectral images for capturing omnidirectional scenes. The system is constructed using two programmable high-speed video cameras with specific lenses and a programmable rotating table. Two different types of color filters are mounted on the two-color video cameras for six-band image acquisition. We present several algorithms for HDR image synthesis, lens distortion correction, image registration, and omnidirectional image synthesis. Spectral power distributions of illuminants (color signals) are recovered from the captured six-band images based on the Wiener estimation algorithm. In this paper, we present two types of applications based on our imaging system: time-lapse imaging and gigapixel imaging. The performance of the proposed system is discussed in detail in terms of the system configurations, acquisition time, artifacts, and spectral estimation accuracy. Experimental results in actual scenes demonstrate that the proposed system is feasible and powerful for acquiring HDR spectral scenes through time-lapse or gigapixel omnidirectional imaging approaches. Finally, we apply the captured omnidirectional images to time-lapse spectral Computer Graphics (CG) renderings and spectral-based relighting of an indoor gigapixel image.

High Dynamic Range Imaging Concept-Based Signal Enhancement Method Reduced the Optical Coherence Tomography Measurement Variability

Abstract: Purpose. To develop and test a novel signal enhancement method for optical coherence tomography (OCT) images based on the high dynamic range (HDR) imaging concept.

Compact in-pixel high dynamic range imaging

Abstract: Embodiments of the invention describe providing a compact solution to provide high dynamic range imaging (HDRI or simply HDR) for an imaging pixel by utilizing a control node for resetting a floating diffusion node to a reference voltage value and for selectively transferring an image charge from a photosensitive element to a readout node. Embodiments of the invention further describe control node to have to a plurality of different capacitance regions to selectively increase the overall capacitance of the floating diffusion node. This variable capacitance of the floating diffusion node increases the dynamic range of the imaging pixel, thereby providing HDR for the host imaging system, as well as increasing the signal-to-noise ratio (SNR) of the imaging system.

High dynamic range imaging & image-based lighting

Abstract: This class outlines recent advances in high dynamic range imaging (HDRI) - from capture to image-based lighting to display. In a hands-on approach, we show how HDR images and video can be captured, the file formats available to store them, and the algorithms required to prepare them for display on low dynamic range displays. The trade-offs at each step are assessed allowing attendees to make informed choices about data capture techniques, file formats and tone reproduction operators. In addition, the latest developments in image-based lighting will be presented.

Joint High Dynamic Range Imaging and Super-Resolution From a Single Image

Abstract: This paper presents a new framework for jointly enhancing the resolution and the dynamic range of an image, i.e. , simultaneous super-resolution (SR) and high dynamic range imaging (HDRI), based on a convolutional neural network (CNN). From the common trends of both tasks, we train a CNN for the joint HDRI and SR by focusing on the reconstruction of high-frequency details. Specifically, the high-frequency component in our work is the reflectance component according to the Retinex-based image decomposition, and only the reflectance component is manipulated by the CNN while another component (illumination) is processed in a conventional way. In training the CNN, we devise an appropriate loss function that contributes to the naturalness quality of resulting images. Experiments show that our algorithm outperforms the cascade implementation of CNN-based SR and HDRI.