For example, the common assumption that the brightest color in an image is white can be hardly justified for high contrast LCD displays, not to mention next generation HDR displays, that can easily create bright highlights and the impression of self-luminous colors. The assumptions of the traditional low-dynamic range imaging, designed for paper print as a major output medium, are ill suited for the range of visual material that is shown on modern displays. The advances in high dynamic range (HDR) imaging, especially in the display and camera technology, have a significant impact on the existing imaging systems. KeywordsOptical flow–Survey–Algorithms–Database–Benchmarks–Evaluation–Metrics In this paper we analyze the results obtained to date and draw a large Improvement in performance has already been achieved. Subsequently a number of researchers have uploaded their results to our website and published papers using the data. We also made the data freely available on the web at. Of our data to establish the current state of the art. In October 2007, we published the performance of several well-known methods on a preliminary version In addition to the average angular error used by Barron etal., we compute the absoluteįlow endpoint error, measures for frame interpolation error, improved statistics, and results at motion discontinuities and Of optical flow algorithms: (1)sequences with nonrigid motion where the ground-truth flow is determined by tracking hiddenįluorescent texture, (2)realistic synthetic sequences, (3)high frame-rate video used to study interpolation error, and (4)modified To that end, we contribute four types of data to test different aspects We propose a new set of benchmarks and evaluation methodsįor the next generation of optical flow algorithms. Nonrigid motion, real sensor noise, and motion discontinuities. Instead, they center on problems associated with complex natural scenes, including The challenges for optical flow algorithms today go beyond the datasets andĮvaluation methods proposed in that paper. (1994) led to significant advances in performance. The quantitative evaluation of optical flow algorithms by Barron et al. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. These artifact maps can be combined to yield a single quality score. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. In this paper, we propose an objective metric which aims to simplify this process. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. Histogram-based ghost detection is proposed, implemented and discussed to simplify its implementation on FPGA architecture. These two methods are implemented in C++ programming language as prototypes. The main focus is given to de-ghosting methods that are reviewed and two of them - a bitmap movement detection based on a median threshold and a histogram based ghost detection - are presented as suitable techniques for a real-time video capturing and implementation on FPGA (Field-Programmable Gate Array) architecture. Basic information about HDRI are presented in this thesis. However, if there is a motion in the scene during a sequence acquisition, a resultant HDR image contains ghosting artefacts due to moving objects in the captured scene. This technique works perfectly only on static scenes. A standard and most common approach to obtain an HDR image is a multiple exposures fusion that consists of combining multiple images of the same scene captured with different exposure times. HDRI technology is becoming increasingly popular in recent years. This thesis deals with a synthesis of high dynamic range imaging (HDRI).
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