We demonstrated that repeated sequence of varying exposure frames can be used for automatic point-spread function estimation and segmentation for challenging object motion blur scenarios
Invertible motion blur in video
ACM Trans. Graph., no. 3 (2009): 1-8
We show that motion blur in successive video frames is invertible even if the point-spread function (PSF) due to motion smear in a single photo is non-invertible. Blurred photos exhibit nulls (zeros) in the frequency transform of the PSF, leading to an ill-posed deconvolution. Hardware solutions to avoid this require specialized devices s...更多
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- Motion blur is a common problem in photographing fast moving objects.
- Consider deblurring a fast moving object in front of a static background.
- Automatic deblurring involves three critical components: (a) maintaining invertible PSF, (b) estimating the motion of the moving parts, and (c) segmenting the moving objects from the static background.
- The authors propose a unique approach based on ordinary cameras and show joint-invertibility of blurs in video frames via the concept of frequency domain null-filling
- Motion blur is a common problem in photographing fast moving objects
- We propose a unique approach based on ordinary cameras and show joint-invertibility of blurs in video frames via the concept of frequency domain null-filling
- We show that by varying the exposure of each frame within a video, point-spread function null-filling can be achieved for object motion
- We showed that motion blur in video can be made invertible by combining non-invertible point-spread function that do not have common zeros
- For a complete deblurring solution, segmentation and point-spread function estimation are as important as point-spread function invertibility
- We demonstrated that repeated sequence of varying exposure frames can be used for automatic point-spread function estimation and segmentation for challenging object motion blur scenarios
- Using the SDK provided with the camera, the exposure time for each frame could be changed .
- The authors placed the object on a variable speed toy train to capture datasets.
- In order to find optimal exposures, the authors bound each exposure within Tmin and Tmax to avoid saturation and unusable photos.
- For N = 3, Tmin = 30 ms and Tmax = 50 ms, the optimized exposures were 30, 35, and 42 ms.
- The authors capture at least 2N images to allow PSF estimation
- The authors showed that motion blur in video can be made invertible by combining non-invertible PSFs that do not have common zeros.
- PSF null-filling can be achieved on machine vision cameras as well as off-the-shelf digital SLR’s using exposure bracketing, without requiring additional hardware or camera motion.
- For a complete deblurring solution, segmentation and PSF estimation are as important as PSF invertibility.
- The authors demonstrated that repeated sequence of varying exposure frames can be used for automatic PSF estimation and segmentation for challenging object motion blur scenarios
- PSF Manipulation: Specialized capture devices employ two important classes of techniques for engineering the PSF to make it (a) invertible and/or (b) invariant. For defocus PSF, wavefront coding [Dowski and Cathey 1995] uses cubic phase plate in front of the lens to make the PSF invariant to scene depths. This can also be achieved by lateral sensor motion [Nagahara et al 2008]. However, these approaches result in defocus blur on the scene parts originally in focus. Coded exposure [Raskar et al 2006] flutters the shutter with a broadband binary code to make the PSF invertible. Accelerating camera motion [Levin et al 2008b] makes the motion PSF invariant to the speed of the object (requiring a priori knowledge of motion direction), at the cost of blurring static parts. Our approach does not modify the camera but indirectly engineers the joint-PSF across frames by carefully choosing the exposure times.
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