72 research outputs found
Understanding Kernel Size in Blind Deconvolution
Most blind deconvolution methods usually pre-define a large kernel size to
guarantee the support domain. Blur kernel estimation error is likely to be
introduced, yielding severe artifacts in deblurring results. In this paper, we
first theoretically and experimentally analyze the mechanism to estimation
error in oversized kernel, and show that it holds even on blurry images without
noises. Then to suppress this adverse effect, we propose a low rank-based
regularization on blur kernel to exploit the structural information in degraded
kernels, by which larger-kernel effect can be effectively suppressed. And we
propose an efficient optimization algorithm to solve it. Experimental results
on benchmark datasets show that the proposed method is comparable with the
state-of-the-arts by accordingly setting proper kernel size, and performs much
better in handling larger-size kernels quantitatively and qualitatively. The
deblurring results on real-world blurry images further validate the
effectiveness of the proposed method.Comment: Accepted by WACV 201
Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring
Video deblurring methods, aiming at recovering consecutive sharp frames from
a given blurry video, usually assume that the input video suffers from
consecutively blurry frames. However, in real-world blurry videos taken by
modern imaging devices, sharp frames usually appear in the given video, thus
making temporal long-term sharp features available for facilitating the
restoration of a blurry frame. In this work, we propose a video deblurring
method that leverages both neighboring frames and present sharp frames using
hybrid Transformers for feature aggregation. Specifically, we first train a
blur-aware detector to distinguish between sharp and blurry frames. Then, a
window-based local Transformer is employed for exploiting features from
neighboring frames, where cross attention is beneficial for aggregating
features from neighboring frames without explicit spatial alignment. To
aggregate long-term sharp features from detected sharp frames, we utilize a
global Transformer with multi-scale matching capability. Moreover, our method
can easily be extended to event-driven video deblurring by incorporating an
event fusion module into the global Transformer. Extensive experiments on
benchmark datasets demonstrate that our proposed method outperforms
state-of-the-art video deblurring methods as well as event-driven video
deblurring methods in terms of quantitative metrics and visual quality. The
source code and trained models are available at
https://github.com/shangwei5/STGTN.Comment: 13 pages, 11 figures, and the code is available at
https://github.com/shangwei5/STGT
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Bounding box regression is the crucial step in object detection. In existing
methods, while -norm loss is widely adopted for bounding box
regression, it is not tailored to the evaluation metric, i.e., Intersection
over Union (IoU). Recently, IoU loss and generalized IoU (GIoU) loss have been
proposed to benefit the IoU metric, but still suffer from the problems of slow
convergence and inaccurate regression. In this paper, we propose a Distance-IoU
(DIoU) loss by incorporating the normalized distance between the predicted box
and the target box, which converges much faster in training than IoU and GIoU
losses. Furthermore, this paper summarizes three geometric factors in bounding
box regression, \ie, overlap area, central point distance and aspect ratio,
based on which a Complete IoU (CIoU) loss is proposed, thereby leading to
faster convergence and better performance. By incorporating DIoU and CIoU
losses into state-of-the-art object detection algorithms, e.g., YOLO v3, SSD
and Faster RCNN, we achieve notable performance gains in terms of not only IoU
metric but also GIoU metric. Moreover, DIoU can be easily adopted into
non-maximum suppression (NMS) to act as the criterion, further boosting
performance improvement. The source code and trained models are available at
https://github.com/Zzh-tju/DIoU.Comment: Accepted to AAAI 2020. The source code and trained models are
available at https://github.com/Zzh-tju/DIo
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