Fast Non-uniform Deblurring using Constrained Camera Pose Subspace

Hu, Zhe; Yang, Ming

doi:10.5244/c.26.136

Cited by 42 publications

(86 citation statements)

References 23 publications

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“…In [30], only three rotations are used to approximate the camera motion, while in-plane translations and rotation are considered in [9,10,11]. It has been shown that these two models achieve similar performance under some assumptions [14].…”

Section: Depth-aware Deblurringmentioning

confidence: 94%

“…Recently, the problem of removing spatially variant blur caused by camera motion has attracted much attention due to its wide range of applications [1,6,25,27,13,30,9,10,28,29,11,12]. Early work splits an image into several regions where each is modeled with a constant PSF and recovered by a uniform deblur algorithm [1,6].…”

Section: Related Workmentioning

confidence: 99%

“…We solve x and W alternatively in a way similar to the other nonuniform deblurring algorithms [30,9,10,11] and explain the detailed steps in Section 3.4.…”

Section: M-stepmentioning

confidence: 99%

“…A similar model has been used to model three degrees of camera motion [9] (with in-plane translations and rotation) instead of camera rotations in [30]. To reduce the computational loads, fast nonuniform deblur methods have been developed to speed up the optimization step [10,11]. In [14], a dataset for motion deblurring is constructed by recording and playing back the camera shake with a robot system.…”

Section: Related Workmentioning

confidence: 99%

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Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

Liu

Yang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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Camera shake during exposure time often results in spatially variant blur effect of the image. The non-uniform blur effect is not only caused by the camera motion, but also the depth variation of the scene. The objects close to the camera sensors are likely to appear more blurry than those at a distance in such cases. However, recent non-uniform deblurring methods do not explicitly consider the depth factor or assume fronto-parallel scenes with constant depth for simplicity. While single image non-uniform deblurring is a challenging problem, the blurry results in fact contain depth information which can be exploited. We propose to jointly estimate scene depth and remove non-uniform blur caused by camera motion via exploiting their underlying geometric relationships, with only single blurry image as input. To this end, we present a unified layer-based model for depth-involved deblurring. We provide a novel layerbased solution using matting to partition the layers and an expectation-maximization scheme to solve this problem. This approach largely reduces the number of unknowns and makes the problem tractable. Experiments on challenging examples demonstrate that both depth and camera shake removal can be well addressed within the unified framework.

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Section: Depth-aware Deblurringmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

“…We solve x and W alternatively in a way similar to the other nonuniform deblurring algorithms [30,9,10,11] and explain the detailed steps in Section 3.4.…”

Section: M-stepmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

Liu

Yang

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

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“…Although these algorithms show promising results, they have heavy computational loads. Zhe Hu [3] had proposed a novel single image deblurring algorithm to remove non-uniform blur with lesser computational load with tolerable redundancy which constrains the camera poses into certain range of rotations.…”

Section: Literature Surveymentioning

confidence: 99%

Face Recognition using TSF Model and DWT based Multilevel Illumination Normalization

2016

IJSR

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A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. One of the ways to do this is by comparing selected facial features from the image with a facial database. Non-uniform blurring situations may arise due to tilts and rotations in hand-held cameras. Also degradations occur due to changes in illumination, pose, expression, partial occlusions etc. Here Non-uniform blur and variations in Illumination are considered. Current Face recognition systems consider the motion blur as a space invariant feature and uses simple convolution model for approximating motion blur. But in natural imaging, motion blur is non-uniform. So for face recognition in the presence of space varying motion blur, a methodology comprising of arbitrarily-shaped kernels is used. The blurred face is modeled as a convex combination of geometrically transformed instances of the focused gallery face using TSF Model. The probe image is compared with the convex combinations to find the best match. To handle illumination variations illumination normalization using DWT is used for the test image.

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Joint Blind Motion Deblurring and Depth Estimation of Light Field

Lee

Park

et al. 2018

Computer Vision – ECCV 2018

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Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods.

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Fast Non-uniform Deblurring using Constrained Camera Pose Subspace

Cited by 42 publications

References 23 publications

Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

Joint Depth Estimation and Camera Shake Removal from Single Blurry Image

Face Recognition using TSF Model and DWT based Multilevel Illumination Normalization

Joint Blind Motion Deblurring and Depth Estimation of Light Field

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