Simultaneous HDR and Optic Flow Computation

Hafner, David Zachary; Demetz, Oliver; Weickert, Joachim

doi:10.1109/icpr.2014.360

Cited by 32 publications

(29 citation statements)

References 26 publications

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“…Moving object selection algorithms, on the other hand, first detect motion regions by inspecting the inconsistencies in the input pixel intensities and then seek to eliminate the corresponding artifacts by using samples from either a single input LDR image [JLW08,LC09,RWPD10] or a subset of the input images that are found to be consistent [GGC * 09, RKC09, RC11, HLL * 11, SSM12, OLK13, SPLC13, GKTT13]. While some studies make use of pixel-wise optical flow correspondences [KUWS03,ST04,JO12,HDW14], others utilize patch-based dense matching methods [POK * 11, SKY * 12, HGP12,HGPS13]. While some studies make use of pixel-wise optical flow correspondences [KUWS03,ST04,JO12,HDW14], others utilize patch-based dense matching methods [POK * 11, SKY * 12, HGP12,HGPS13].…”

Section: Related Workmentioning

confidence: 99%

An Objective Deghosting Quality Metric for HDR Images

Tursun

Akyüz

Erdem

et al. 2016

Computer Graphics Forum

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a) Moving people generate blending (red) and visual difference (blue) artifacts. (b) Over-smoothing gives rise to gradient inconsistency (green) artifacts.Figure 1: Our metric detects several kinds of HDR deghosting artifacts. In (a), Khan et al.'s [KAR06] output is shown in the bottom-left corner and our metric's result in the bottom-right. The same for (b), except Hu et al.'s [HGPS13] deghosting algorithm is used. Exposure sequences are shown on the top. Cyan color occurs due to both gradient and visual difference metrics producing high output. AbstractReconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. 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the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. 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%. 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.

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Section: Related Workmentioning

confidence: 99%

An Objective Deghosting Quality Metric for HDR Images

Tursun

Akyüz

Erdem

et al. 2016

Computer Graphics Forum

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“…Hafner et al [HDW14] propose an energy-minimization approach which simultaneously calculates HDR irradiance together with the displacement fields. The displacement fields have sub-pixel accuracy, similar to Zimmer et al [ZBW11].…”

Section: Optical-flow Basedmentioning

confidence: 99%

The State of the Art in HDR Deghosting: A Survey and Evaluation

Tursun

Akyüz

Erdem

et al. 2015

Computer Graphics Forum

100

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Obtaining a high quality high dynamic range (HDR) image in the presence of camera and object movement has been a long-standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: (1) how to detect ghost regions and (2) what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under-and over-exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state-of-the-art deghosting algorithms.

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“…There is a large body of work on HDR image registration and deghosting, facilitating HDR exposure bracketing of dynamic scenes. For example, for per-pixel registration optical flow can be used [108,125,280], or patch-based approaches [118,224]. For a thorough survey and categorization, we refer to the state-of-the-art report by Tursun et al [244].…”

Section: Temporally Multiplexed Exposuresmentioning

confidence: 99%

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

Eilertsen¹

2018

Linköping Studies in Science and Technology. Dissertations

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Simultaneous HDR and Optic Flow Computation

Cited by 32 publications

References 26 publications

An Objective Deghosting Quality Metric for HDR Images

An Objective Deghosting Quality Metric for HDR Images

The State of the Art in HDR Deghosting: A Survey and Evaluation

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

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