5,226 research outputs found
Generalized Video Deblurring for Dynamic Scenes
Several state-of-the-art video deblurring methods are based on a strong
assumption that the captured scenes are static. These methods fail to deblur
blurry videos in dynamic scenes. We propose a video deblurring method to deal
with general blurs inherent in dynamic scenes, contrary to other methods. To
handle locally varying and general blurs caused by various sources, such as
camera shake, moving objects, and depth variation in a scene, we approximate
pixel-wise kernel with bidirectional optical flows. Therefore, we propose a
single energy model that simultaneously estimates optical flows and latent
frames to solve our deblurring problem. We also provide a framework and
efficient solvers to optimize the energy model. By minimizing the proposed
energy function, we achieve significant improvements in removing blurs and
estimating accurate optical flows in blurry frames. Extensive experimental
results demonstrate the superiority of the proposed method in real and
challenging videos that state-of-the-art methods fail in either deblurring or
optical flow estimation.Comment: CVPR 2015 ora
Real-time Global Illumination Decomposition of Videos
We propose the first approach for the decomposition of a monocular color
video into direct and indirect illumination components in real time. We
retrieve, in separate layers, the contribution made to the scene appearance by
the scene reflectance, the light sources and the reflections from various
coherent scene regions to one another. Existing techniques that invert global
light transport require image capture under multiplexed controlled lighting, or
only enable the decomposition of a single image at slow off-line frame rates.
In contrast, our approach works for regular videos and produces temporally
coherent decomposition layers at real-time frame rates. At the core of our
approach are several sparsity priors that enable the estimation of the
per-pixel direct and indirect illumination layers based on a small set of
jointly estimated base reflectance colors. The resulting variational
decomposition problem uses a new formulation based on sparse and dense sets of
non-linear equations that we solve efficiently using a novel alternating
data-parallel optimization strategy. We evaluate our approach qualitatively and
quantitatively, and show improvements over the state of the art in this field,
in both quality and runtime. In addition, we demonstrate various real-time
appearance editing applications for videos with consistent illumination
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