119 research outputs found
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
Multiframe visual-inertial blur estimation and removal for unmodified smartphones
Pictures and videos taken with smartphone cameras often suffer from motion blur due to handshake during the
exposure time. Recovering a sharp frame from a blurry one is an ill-posed problem but in smartphone applications
additional cues can aid the solution. We propose a blur removal algorithm that exploits information from subsequent
camera frames and the built-in inertial sensors of an unmodified smartphone. We extend the fast non-blind
uniform blur removal algorithm of Krishnan and Fergus to non-uniform blur and to multiple input frames. We estimate
piecewise uniform blur kernels from the gyroscope measurements of the smartphone and we adaptively steer
our multiframe deconvolution framework towards the sharpest input patches. We show in qualitative experiments
that our algorithm can remove synthetic and real blur from individual frames of a degraded image sequence within
a few seconds
High Dynamic Range Imaging with Context-aware Transformer
Avoiding the introduction of ghosts when synthesising LDR images as high
dynamic range (HDR) images is a challenging task. Convolutional neural networks
(CNNs) are effective for HDR ghost removal in general, but are challenging to
deal with the LDR images if there are large movements or
oversaturation/undersaturation. Existing dual-branch methods combining CNN and
Transformer omit part of the information from non-reference images, while the
features extracted by the CNN-based branch are bound to the kernel size with
small receptive field, which are detrimental to the deblurring and the recovery
of oversaturated/undersaturated regions. In this paper, we propose a novel
hierarchical dual Transformer method for ghost-free HDR (HDT-HDR) images
generation, which extracts global features and local features simultaneously.
First, we use a CNN-based head with spatial attention mechanisms to extract
features from all the LDR images. Second, the LDR features are delivered to the
Hierarchical Dual Transformer (HDT). In each Dual Transformer (DT), the global
features are extracted by the window-based Transformer, while the local details
are extracted using the channel attention mechanism with deformable CNNs.
Finally, the ghost free HDR image is obtained by dimensional mapping on the HDT
output. Abundant experiments demonstrate that our HDT-HDR achieves the
state-of-the-art performance among existing HDR ghost removal methods.Comment: 8 pages, 5 figure
Correct spatially varying image blur by Projective Motion Richardson-Lucy Algorithm and Blur Image alignment
Master'sMASTER OF ENGINEERIN
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