2 research outputs found
Prior-enlightened and Motion-robust Video Deblurring
Various blur distortions in video will cause negative impact on both human
viewing and video-based applications, which makes motion-robust deblurring
methods urgently needed. Most existing works have strong dataset dependency and
limited generalization ability in handling challenging scenarios, like blur in
low contrast or severe motion areas, and non-uniform blur. Therefore, we
propose a PRiOr-enlightened and MOTION-robust video deblurring model
(PROMOTION) suitable for challenging blurs. On the one hand, we use 3D group
convolution to efficiently encode heterogeneous prior information, explicitly
enhancing the scenes' perception while mitigating the output's artifacts. On
the other hand, we design the priors representing blur distribution, to better
handle non-uniform blur in spatio-temporal domain. Besides the classical camera
shake caused global blurry, we also prove the generalization for the downstream
task suffering from local blur. Extensive experiments demonstrate we can
achieve the state-of-the-art performance on well-known REDS and GoPro datasets,
and bring machine task gain.Comment: 26 pages, 13 figures, and 7 table
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
Blind image deblurring is a fundamental and challenging computer vision
problem, which aims to recover both the blur kernel and the latent sharp image
from only a blurry observation. Despite the superiority of deep learning
methods in image deblurring have displayed, there still exists major challenge
with various non-uniform motion blur. Previous methods simply take all the
image features as the input to the decoder, which handles different degrees
(e.g. large blur, small blur) simultaneously, leading to challenges for sharp
image generation. To tackle the above problems, we present a deep two-branch
network to deal with blurry images via a component divided module, which
divides an image into two components based on the representation of blurry
degree. Specifically, two component attentive blocks are employed to learn
attention maps to exploit useful deblurring feature representations on both
large and small blurry regions. Then, the blur-aware features are fed into
two-branch reconstruction decoders respectively. In addition, a new feature
fusion mechanism, orientation-based feature fusion, is proposed to merge sharp
features of the two branches. Both qualitative and quantitative experimental
results show that our method performs favorably against the state-of-the-art
approaches