Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging

Hadziabdic, Kanita Karaduzovic; Telalović, Jasminka Hasić; Mantiuk, Rafał

doi:10.1145/2508244.2508247

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Karaduzovic et al [HTM13] conducted the first subjective experiment of this kind, in which 30 participants were asked to perform pairwise comparisons of four deghosting algorithms on nine different realworld scenes with varying complexity. Recently, subjective evaluation of HDR deghosting algorithms have begun to appear.…”

Section: Related Workmentioning

confidence: 99%

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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the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis.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%

“…As in all fields, the proliferation of these algorithms gave rise to subjective experiments that aim to evaluate their performance [HT13,HTM13,HTM14,TAEE15]. However, subjective comparisons of HDR deghosting algorithms is problematic for several reasons.…”

Section: Introductionmentioning

confidence: 99%

An Objective Deghosting Quality Metric for HDR Images

Tursun

Akyüz

Erdem

et al. 2016

Computer Graphics Forum

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“…It was found that Sen et al's algorithm has the fewest artifacts. In their more recent work [HTM14], the authors extended their comparison to include the algorithm of Hu et al [HGPS13] and performed an expert evaluation.…”

Section: Related Workmentioning

confidence: 99%

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

Tursun

Akyüz

Erdem

et al. 2015

Computer Graphics Forum

102

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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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“…The need to validate methods also motivates comparative studies, in which several state-of-the-art algorithms are evaluated in a subjective experiment. Studies like this have been performed for image aspect ratio retargeting [76], image deghosting [29] or inverse tone-mapping [8,58]. However, probably the most attention has attracted the problem of tonemapping, which is discussed below.…”

Section: Image and Video Quality Assessmentmentioning

confidence: 99%

Quality Assessment in Computer Graphics

Lavoué

Mantiuk

2014

Visual Signal Quality Assessment

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In this chapter, we review the existing works regarding visual quality assessment in computer graphics. This broad area of research includes many subdomains which make intensive use of quality assessment and/or artefact visibility evaluation: geometry processing, rendering, HDR imaging, tone mapping and stereo vision. For each of these sub-domains, we present the existing objective quality metrics, the subjective quality experiments as well as an evaluation and comparison of their performance. We broadly classify these existing works into image-based (i.e. evaluating artefacts introduced in 2D rendered images and videos) and model-based approaches (i.e. artefacts introduced on the 3D models themselves). Finally, the last part presents the emerging trends and main future directions.

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Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging

Cited by 18 publications

References 17 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

Quality Assessment in Computer Graphics

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