Proceedings ArticleDOI
Dynamic Scene Deblurring
Tae Hyun Kim,Byeongjoo Ahn,Kyoung Mu Lee +2 more
- pp 3160-3167
TLDR
This paper proposes a novel energy model designed with the weighted sum of multiple blur data models, which estimates different motion blurs and their associated pixel-wise weights, and resulting sharp image and demonstrates that this method outperforms conventional approaches in deblurring both dynamic scenes and static scenes.Abstract:
Most conventional single image deblurring methods assume that the underlying scene is static and the blur is caused by only camera shake. In this paper, in contrast to this restrictive assumption, we address the deblurring problem of general dynamic scenes which contain multiple moving objects as well as camera shake. In case of dynamic scenes, moving objects and background have different blur motions, so the segmentation of the motion blur is required for deblurring each distinct blur motion accurately. Thus, we propose a novel energy model designed with the weighted sum of multiple blur data models, which estimates different motion blurs and their associated pixel-wise weights, and resulting sharp image. In this framework, the local weights are determined adaptively and get high values when the corresponding data models have high data fidelity. And, the weight information is used for the segmentation of the motion blur. Non-local regularization of weights are also incorporated to produce more reliable segmentation results. A convex optimization-based method is used for the solution of the proposed energy model. Experimental results demonstrate that our method outperforms conventional approaches in deblurring both dynamic scenes and static scenes.read more
Citations
More filters
Proceedings ArticleDOI
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
TL;DR: This work proposes a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources and presents a new large-scale dataset that provides pairs of realistic blurry image and the corresponding ground truth sharp image that are obtained by a high-speed camera.
Proceedings ArticleDOI
Scale-Recurrent Network for Deep Image Deblurring
TL;DR: A Scale-recurrent Network (SRN-DeblurNet) is proposed and shown to produce better quality results than state-of-the-arts, both quantitatively and qualitatively in single image deblurring.
Proceedings ArticleDOI
Multi-Stage Progressive Image Restoration
Syed Waqas Zamir,Aditya Arora,Salman Khan,Munawar Hayat,Fahad Shahbaz Khan,Ming-Hsuan Yang,Ling Shao +6 more
TL;DR: MPRNet as discussed by the authors proposes a multi-stage architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps, and introduces a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Proceedings ArticleDOI
From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur
Dong Gong,Jie Yang,Lingqiao Liu,Yanning Zhang,Ian Reid,Chunhua Shen,Anton van den Hengel,Qinfeng Shi +7 more
TL;DR: This work directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow and is the first universal end-to-end mapping from the blur image to the dense motion flow.
Proceedings ArticleDOI
Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks
TL;DR: Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms in terms of accuracy, speed, and model size.
References
More filters
Book
Regularization of Inverse Problems
TL;DR: Inverse problems have been studied in this article, where Tikhonov regularization of nonlinear problems has been applied to weighted polynomial minimization problems, and the Conjugate Gradient Method has been used for numerical realization.
Journal ArticleDOI
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Antonin Chambolle,Thomas Pock +1 more
TL;DR: A first-order primal-dual algorithm for non-smooth convex optimization problems with known saddle-point structure can achieve O(1/N2) convergence on problems, where the primal or the dual objective is uniformly convex, and it can show linear convergence, i.e. O(ωN) for some ω∈(0,1), on smooth problems.
Journal ArticleDOI
Removing camera shake from a single photograph
TL;DR: This work introduces a method to remove the effects of camera shake from seriously blurred images, which assumes a uniform camera blur over the image and negligible in-plane camera rotation.
Journal ArticleDOI
High-quality motion deblurring from a single image
Qi Shan,Jiaya Jia,Aseem Agarwala +2 more
TL;DR: A new algorithm for removing motion blur from a single image is presented using a unified probabilistic model of both blur kernel estimation and unblurred image restoration and is able to produce high quality deblurred results in low computation time.
Proceedings Article
Fast Image Deconvolution using Hyper-Laplacian Priors
Dilip Krishnan,Rob Fergus +1 more
TL;DR: This paper describes a deconvolution approach that is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors and is able to deconvolve a 1 megapixel image in less than ~3 seconds, achieving comparable quality to existing methods that take ~20 minutes.