61 research outputs found
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that
is the recovery of a sequence of high-resolution images from its low-resolution
counterpart. Our approach is based on a "sequential" model (i.e., each
high-resolution frame is supposed to be a displaced version of the preceding
one) and considers the use of sparsity-enforcing priors. Both the recovery of
the high-resolution images and the motion fields relating them is tackled. This
leads to a large-dimensional, non-convex and non-smooth problem. We propose an
algorithmic framework to address the latter. Our approach relies on fast
gradient evaluation methods and modern optimization techniques for
non-differentiable/non-convex problems. Unlike some other previous works, we
show that there exists a provably-convergent method with a complexity linear in
the problem dimensions. We assess the proposed optimization method on {several
video benchmarks and emphasize its good performance with respect to the state
of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201
End-to-End Learning of Video Super-Resolution with Motion Compensation
Learning approaches have shown great success in the task of super-resolving
an image given a low resolution input. Video super-resolution aims for
exploiting additionally the information from multiple images. Typically, the
images are related via optical flow and consecutive image warping. In this
paper, we provide an end-to-end video super-resolution network that, in
contrast to previous works, includes the estimation of optical flow in the
overall network architecture. We analyze the usage of optical flow for video
super-resolution and find that common off-the-shelf image warping does not
allow video super-resolution to benefit much from optical flow. We rather
propose an operation for motion compensation that performs warping from low to
high resolution directly. We show that with this network configuration, video
super-resolution can benefit from optical flow and we obtain state-of-the-art
results on the popular test sets. We also show that the processing of whole
images rather than independent patches is responsible for a large increase in
accuracy.Comment: Accepted to GCPR201
Towards Interpretable Video Super-Resolution via Alternating Optimization
In this paper, we study a practical space-time video super-resolution (STVSR)
problem which aims at generating a high-framerate high-resolution sharp video
from a low-framerate low-resolution blurry video. Such problem often occurs
when recording a fast dynamic event with a low-framerate and low-resolution
camera, and the captured video would suffer from three typical issues: i)
motion blur occurs due to object/camera motions during exposure time; ii)
motion aliasing is unavoidable when the event temporal frequency exceeds the
Nyquist limit of temporal sampling; iii) high-frequency details are lost
because of the low spatial sampling rate. These issues can be alleviated by a
cascade of three separate sub-tasks, including video deblurring, frame
interpolation, and super-resolution, which, however, would fail to capture the
spatial and temporal correlations among video sequences. To address this, we
propose an interpretable STVSR framework by leveraging both model-based and
learning-based methods. Specifically, we formulate STVSR as a joint video
deblurring, frame interpolation, and super-resolution problem, and solve it as
two sub-problems in an alternate way. For the first sub-problem, we derive an
interpretable analytical solution and use it as a Fourier data transform layer.
Then, we propose a recurrent video enhancement layer for the second sub-problem
to further recover high-frequency details. Extensive experiments demonstrate
the superiority of our method in terms of quantitative metrics and visual
quality.Comment: ECCV 202
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Video Super-Resolution (VSR) aims to recover sequences of high-resolution
(HR) frames from low-resolution (LR) frames. Previous methods mainly utilize
temporally adjacent frames to assist the reconstruction of target frames.
However, in the real world, there is a lot of irrelevant information in
adjacent frames of videos with fast scene switching, these VSR methods cannot
adaptively distinguish and select useful information. In contrast, with a
transformer structure suitable for temporal tasks, we devise a novel adaptive
scenario video super-resolution method. Specifically, we use optical flow to
label the patches in each video frame, only calculate the attention of patches
with the same label. Then select the most relevant label among them to
supplement the spatial-temporal information of the target frame. This design
can directly make the supplementary information come from the same scene as
much as possible. We further propose a cross-scale feature aggregation module
to better handle the scale variation problem. Compared with other video
super-resolution methods, our method not only achieves significant performance
gains on single-scene videos but also has better robustness on cross-scene
datasets
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