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.

Radiometric calibration of wide-field camera system with an application in astronomy

Abstract: Camera response function (CRF) is widely used for the description of the relationship between scene radiance and image brightness. Most common application of CRF is High Dynamic Range (HDR) reconstruction of the radiance maps of imaged scenes from a set of frames with different exposures. The main goal of this work is to provide an overview of CRF estimation algorithms and compare their outputs with results obtained under laboratory conditions. These algorithms, typically designed for multimedia content, are unfortunately quite useless with astronomical image data, mostly due to their nature (blur, noise, and long exposures). Therefore, we propose an optimization of selected methods to use in an astronomical imaging application. Results are experimentally verified on the wide-field camera system using Digital Single Lens Reflex (DSLR) camera.

High dynamic range imaging using vector space projection

Abstract: The purpose of HDR imaging is to increase the dynamic range of the image intensity of a scene which otherwise would be limited by a normal imaging technique/device. A commonly used approach to HDR is to acquire images with multiple exposures of the same scene. In this paper, an iterative algorithm is explored for the first time to recover an HDR image from multiple exposures based on the method of projections onto constraint sets. To construct appropriate constraint sets, noise sources in pixels and sensor structures are discussed. An equivalent noise characteristics at the sensor output is modeled as the Gaussian Random Process. Base on noise characteristics and sensor response features, a group of pixel-wise constraint sets are defined to measure the estimation errors. Utilizing the knowledge on neighbourhood pixels, the local spatial constraint sets on the image are also defined. The method of vector-space projections is applied onto these constraint sets to reconstruct the HDR image using multiple exposures of the same scene.

Facial expression recognition under harsh lighting using high dynamic range imaging

Abstract: Facial information can reveal the emotional status of individuals. Although traditional cameras can capture this information, such cameras struggle to acquire the necessary information in extreme lighting conditions. This thesis aim to investigate whether High Dynamic Range (HDR) imaging can capture human facial expression under complex lighting conditions, and in doing so, enhance Facial Expression Recognition (FER) performance. The techniques presented in this thesis focus on developing a baseline for images captured in scenes with harsh lighting conditions, where Low Dynamic Range (LDR) images have difficulty capturing the full range of light in a single exposure. The thesis considers unprocessed images and a variety of pre-processing methods to examine whether reducing the impact of large lighting variations could improve the quality of an input image. In addition, realistic facial data plays a key role in validating facial expression analysis systems. Today, the majority of FER algorithms are evaluated only on images generated in highly controlled laboratory environments. The variability of a facial appearance in an image could be dominated by changes in head pose and illumination conditions. This can effectively hide features that are necessary to discriminate different subjects or different facial articulations. New HDR imaging techniques are thus introduced to help ensure that all the details in a scene is captured no matter what the lighting conditions present, and all this detail is then available to the FER algorithms. This is also investigated on Face recognition algorithms.

Subjective evaluation of high dynamic range imaging for face matching

Abstract: Human facial recognition in the context of surveillance, forensics and photo-ID verification is a task for which accuracy is critical. Quite often limitations in the overall quality of facial images reduces individuals' ability in taking decisions regarding a person's identity. To verify the suitability of advanced imaging techniques to improve individuals' performance in face matching we investigate how High Dynamic Range (HDR) imaging compares with traditional low (or standard) dynamic range (LDR) imaging in a facial recognition task. An HDR face dataset with five different lighting conditions is created. Subsequently, this dataset is used in a controlled experiment (N=40) to measure performance and accuracy of human participants when identifying faces in HDR vs LDR. Results demonstrate that face matching accuracy and reaction time are improved significantly by HDR imaging. This work demonstrates scope for realistic image reproduction and delivery in face matching tasks and suggests that security systems could benefit from the adoption of HDR imaging techniques.

Artifact-free high dynamic range imaging based on Sandroid Cubesat with camera array

Abstract: Space Imaging face serious dynamic range problem. Images captured by conventional cameras will loss important information of overexposed or underexposed regions. In this paper, we proposed a novel HDR imaging method with camera array based on Sandroid Cubesat to obtain accurate images of space target. It has been introduced from two aspects of hardware and software and has been tested the accuracy of variety space conditions in DST environment. Experiments proof that our method can obtain clear and artifact-free images from high dynamic range scene, and it cost only a little computation. As the platform of our approach, Sandroid Cubesat is a small satellite with smart phone cluster as the core. It carries powerful computing capability, which could be used to solve complex image processing algorithms on-board.