Optical Flow Guided TV-L1 Video Interpolation and Restoration

Werlberger, Manuel; Pock, Thomas; Unger, Markus; Bischof, Horst

doi:10.1007/978-3-642-23094-3_20

Cited by 61 publications

(46 citation statements)

References 26 publications

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“…Optical flow can also be a determinant feature in video indexing and retrieval [123,202,230]. Video restoration of aged films is another field of interest, and can be improved by taking optical flow estimation into account [92,270].…”

Section: Applicative Domainsmentioning

confidence: 99%

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

333

174

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a b s t r a c tOptical flow estimation is one of the oldest and still most active research domains in computer vision. In 35 years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.

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Section: Applicative Domainsmentioning

confidence: 99%

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

333

174

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“…In contrast to our approach, view interpolation is only done for the background geometry and concealed with a strong motion blur. Werlberger et al [26] and Chen and Lorenz [2] addressed temporal image interpolation and suggested the use of optical flow correspondences. Mahajan et al [14] estimated gradient rather than pixel correspondences.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge feature-based spatiotemporal view point interpolation has not been explored in literature yet. In contrast to works like [1,23,14,26] we do not impose strong assumptions on the scene structure or the camera movement, allowing for arbitrary, uncalibrated input. In several experiments we study the strengths and weaknesses of sparse matching techniques for image interpolation on a variety of sequences.…”

Section: Contributionmentioning

confidence: 99%

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Spatial and Temporal Interpolation of Multi-view Image Sequences

Gurdan

Oswald

Gurdan³

et al. 2014

Lecture Notes in Computer Science

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Abstract. We propose a simple and effective framework for multi-view image sequence interpolation in space and time. For spatial view point interpolation we present a robust feature-based matching algorithm that allows for wide-baseline camera configurations. To this end, we introduce two novel filtering approaches for outlier elimination and a robust approach for match extrapolations at the image boundaries. For smallbaseline and temporal interpolations we rely on an established optical flow based approach. We perform a quantitative and qualitative evaluation of our framework and present applications and results. Our method has a low runtime and results can compete with state-of-the-art methods.

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“…This is often solved by warping the flow to the temporal position of the intermediate frame I1 /2 (see e.g. [9], [10], [11]), in which one defines a new flow v…”

Section: Motion Compensated Frame Interpolationmentioning

confidence: 99%

Motion Compensated Frame Interpolation with a Symmetric Optical Flow Constraint

Rakêt

Roholm

Bruhn

et al. 2012

Advances in Visual Computing

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Abstract. We consider the problem of interpolating frames in an image sequence. For this purpose accurate motion estimation can be very helpful. We propose to move the motion estimation from the surrounding frames directly to the unknown frame by parametrizing the optical flow objective function such that the interpolation assumption is directly modeled. This reparametrization is a powerful trick that results in a number of appealing properties, in particular the motion estimation becomes more robust to noise and large displacements, and the computational workload is more than halved compared to usual bidirectional methods. The proposed reparametrization is generic and can be applied to almost every existing algorithm. In this paper we illustrate its advantages by considering the classic TV-L 1 optical flow algorithm as a prototype. We demonstrate that this widely used method can produce results that are competitive with current state-of-the-art methods. Finally we show that the scheme can be implemented on graphics hardware such that it becomes possible to double the frame rate of 640 × 480 video footage at 30 fps, i.e. to perform frame doubling in realtime.

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Optical Flow Guided TV-L1 Video Interpolation and Restoration

Cited by 61 publications

References 26 publications

Optical flow modeling and computation: A survey

Optical flow modeling and computation: A survey

Spatial and Temporal Interpolation of Multi-view Image Sequences

Motion Compensated Frame Interpolation with a Symmetric Optical Flow Constraint

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