Filters








37,793 Hits in 2.1 sec

Modeling Blurred Video with Layers [chapter]

Jonas Wulff, Michael Julian Black
2014 Lecture Notes in Computer Science  
We address this with a novel layered model of scenes in motion. From a motion-blurred video sequence, we jointly estimate the layer segmentation and each layer's appearance and motion.  ...  Since the blur is a function of the layer motion and segmentation, it is completely determined by our generative model.  ...  Adelson for insights into motion blur and layers, and R. Zavada for the JFK video.  ... 
doi:10.1007/978-3-319-10599-4_16 fatcat:rre7ovcpafguzmpo2mgnqc2zdq

Occlusion-Aware Video Deblurring with a New Layered Blur Model [article]

Byeongjoo Ahn, Tae Hyun Kim, Wonsik Kim, Kyoung Mu Lee
2016 arXiv   pre-print
We present a deblurring method for scenes with occluding objects using a carefully designed layered blur model.  ...  Layered blur model is frequently used in the motion deblurring problem to handle locally varying blurs, which is caused by object motions or depth variations in a scene.  ...  Proposed Layered Blur Model Here we propose a new layered blur model.  ... 
arXiv:1611.09572v1 fatcat:jqq2y5psazcyvbl7j5mruvdal4

Dataset and Network Structure: Towards Frames Selection for Fast Video Deblurring

Abdelwahed Nahli, Shan Cao, Jia Zhiwei, Runze Ma, Shugong Xu
2021 IEEE Access  
Wulff and Black [58] presented a double-layered blur model that can have different blur statuses in the front and back layer segments. Kohler et al.  ...  [62] introduced REDs dataset, which consists of 330 video pairs, each video with 100 frames, he claimed that the used data degradation model can produce more realistic motion blur. Su et al.  ... 
doi:10.1109/access.2021.3074199 fatcat:jcqk6tkydnbkhcn6opgnpkjzja

Privacy-Aware Activity Classification from First Person Office Videos [article]

Partho Ghosh, Md. Abrar Istiak, Nayeeb Rashid, Ahsan Habib Akash, Ridwan Abrar, Ankan Ghosh Dastider, Asif Shahriyar Sushmit, Taufiq Hasan
2020 arXiv   pre-print
We utilized a Mask-RCNN with an Inception-ResNet hybrid as a feature extractor for detecting, and then blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos.  ...  On privacy protected videos, the performances were slightly degraded, with accuracy, precision, recall, and F1 scores at 73.68%, 0.79, 0.75, and 0.74, respectively.  ...  The authors of [29] used AlexNet [30] as the feature extractor with two uni-directional LSTM layers for temporal sequence modeling.  ... 
arXiv:2006.06246v1 fatcat:hhvslmca4nbqxde72zuov263s4

Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition

Changxing Ding, Dacheng Tao
2018 IEEE Transactions on Pattern Analysis and Machine Intelligence  
With the proposed techniques, we also obtain the first place in the BTAS 2016 Video Person Recognition Evaluation.  ...  TBE-CNN is an end-to-end model that extracts features efficiently by sharing the low- and middle-level convolutional layers between the trunk and branch networks.  ...  [9] proposed pre-training CNN models with a large volume of still face images and then fine-tuning the CNN models with small real-world video databases.  ... 
doi:10.1109/tpami.2017.2700390 pmid:28475048 fatcat:upxmdoqzo5dnri5zn6sdm7xnmi

Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring

Wenjia Niu, Kewen Xia, Yongke Pan
2021 Symmetry  
Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods.  ...  Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel  ...  Our D model consists of 16 convolution layers and one top layer with a bidirectional soft-max classifier.  ... 
doi:10.3390/sym13040630 fatcat:2rwr3ndp7jaa7lv55zkgmrsbjq

Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel [article]

Wenqi Ren, Jinshan Pan, Xiaochun Cao, Ming-Hsuan Yang
2017 arXiv   pre-print
The proposed blur model is based on the non-linear optical flow, which describes complex motion blur more effectively.  ...  Video deblurring is a challenging problem as the blur is complex and usually caused by the combination of camera shakes, object motions, and depth variations.  ...  To deal with complex motion blurs, layered blur model is developed in the deblurring problem to handle locally varying blurs [5, 39] . Cho et al.  ... 
arXiv:1708.03423v1 fatcat:ztfjqbiyajcubatwzihhvmbrxe

Video Deblurring via Semantic Segmentation and Pixel-Wise Non-linear Kernel

Wenqi Ren, Jinshan Pan, Xiaochun Cao, Ming-Hsuan Yang
2017 2017 IEEE International Conference on Computer Vision (ICCV)  
The proposed blur model is based on the non-linear optical flow, which describes complex motion blur more effectively.  ...  Video deblurring is a challenging problem as the blur is complex and usually caused by the combination of camera shakes, object motions, and depth variations.  ...  To deal with complex motion blurs, layered blur model is developed in the deblurring problem to handle locally varying blurs [5, 39] . Cho et al.  ... 
doi:10.1109/iccv.2017.123 dblp:conf/iccv/RenPC017 fatcat:cvz7fjcrwrfqzipu63tzxre5vm

Online Video Deblurring via Dynamic Temporal Blending Network [article]

Tae Hyun Kim, Kyoung Mu Lee, Bernhard Schölkopf, Michael Hirsch
2017 arXiv   pre-print
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.  ...  In doing so, our network is able to remove even large blur caused by strong camera shake and/or fast moving objects.  ...  Wulff and Black [37] consider differently blurred bi-layered scenes and estimate segment-wise accurate blur kernels by constraining those through a temporally consistent affine motion model.  ... 
arXiv:1704.03285v1 fatcat:jkfg6cdkeba6ncizkokr5g2ti4

Spatio-Temporal Filter Adaptive Network for Video Deblurring [article]

Shangchen Zhou, Jiawei Zhang, Jinshan Pan, Haozhe Xie, Wangmeng Zuo, Jimmy Ren
2019 arXiv   pre-print
We then propose the new Filter Adaptive Convolutional (FAC) layer to align the deblurred features of the previous frame with the current frame and remove the spatially variant blur from the features of  ...  of accuracy, speed as well as model size.  ...  The algorithms by [42] and [32] use the predicted optical flow to segment layers with different blur and estimate the blur layer-by-layer. In addition, Kim et al.  ... 
arXiv:1904.12257v2 fatcat:253n774rnnhc5nhbvo7iug4kti

Transfer Learning and Decision Fusion for Real Time Distortion Classification in Laparoscopic Videos

Nouar AlDahoul, Hezerul Abdul Karim, Myles Joshua Toledo Tan, Jamie Ledesma Fermin
2021 IEEE Access  
This challenging dataset contains videos that have five types of distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur with four levels of intensity.  ...  It is done with the aid of a video camera. Laparoscopic videos are affected by various distortions during surgery which lead to loss of visual quality.  ...  The VGG model created by the Visual Geometry Group from the University of Oxford used a very deep CNN with 16 weight layers, 13 of which were convolutional layers with 3 × 3 filters, while three were fully  ... 
doi:10.1109/access.2021.3105454 fatcat:6atlrnzkcraenholfgxgxy4boe

Spatio-temporal deep learning model for distortion classification in laparoscopic video

Nouar AlDahoul, Hezerul Abdul Karim, Abdulaziz Saleh Ba Wazir, Myles Joshua Toledo Tan, Mohammad Faizal Ahmad Fauzi
2021 F1000Research  
The laparoscopic video is prone to various distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur.  ...  Additionally, the proposed distortion identification model is able to run in real-time with low inference time (0.15 sec).  ...  The numbers of videos for each label or distortion are not balanced (300 videos with AWGN, 320 videos with smoke, 400 videos with uneven illumination, 160 videos with defocus blur, 80 videos with motion  ... 
doi:10.12688/f1000research.72980.1 fatcat:bzbc4smonbe7dayge64xqyweru

Vehicle Classification with Convolutional Neural Network on Motion Blurred Images

QIAOJIN GUO, ZHONGYAN LIANG, JIE HU
2017 DEStech Transactions on Computer Science and Engineering  
However, the trained model achieves poor performance on motion blurred images captured from videos. This paper proposes a new method for dealing with motion blurred images.  ...  Random blurred images are generated during training in order to optimize the network parameters.  ...  However, the trained model achieves poor performance on motion blurred images captured from videos. This paper proposes a new method for dealing with motion blurred images.  ... 
doi:10.12783/dtcse/aiea2017/14912 fatcat:jzksqhp7mzbcpobplgypndzurq

Learning to Synthesize Motion Blur

Tim Brooks, Jonathan T. Barron
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We additionally capture a high quality test set of real motion blurred images, synthesized from slow motion videos, with which we evaluate our model against several baseline techniques that can be used  ...  To build this system we motivate and design a differentiable "line prediction" layer to be used as part of a neural network architecture, with which we can learn a system to regress from image pairs to  ...  Direct Prediction: instead of using line prediction our network directly estimates the motion blurred image, by replacing our line prediction model with a single 1×1 conv layer that produces a 3 channel  ... 
doi:10.1109/cvpr.2019.00700 dblp:conf/cvpr/BrooksB19 fatcat:rwphouqiyratxbmeluacvn5laq

Video Deblurring via Temporally and Spatially Variant Recurrent Neural Network

Runhua Jiang, Li Zhao, Tao Wang, Jinxin Wang, Xiaoqin Zhang
2019 IEEE Access  
However, it is hard to recover sharp videos using existing single or multiple image deblurring methods, as the blur artifacts in blurry videos are both temporally and spatially varying.  ...  Meanwhile, the proposed model is trained in an end-to-end manner, where the model input and output are set to the same number.  ...  VIDEO DEBLURRING Compared with image deblurring, video deblurring is more challenging as it needs consider the problem of modeling temporal information.  ... 
doi:10.1109/access.2019.2962505 fatcat:ppivl5qz2baufgyy5cntoju3k4
« Previous Showing results 1 — 15 out of 37,793 results