Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs
ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Image and Vision Computing
Image deblurring using inertial measurement sensors
ACM SIGGRAPH 2010 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Single image deblurring using motion density functions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems
IEEE Transactions on Signal Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This paper addresses the issue of non-uniform motion deblurring due to hand shake for a single photograph. The main difficulty of spatially variant motion deblurring is that the deconvolution algorithm can not directly be used to estimate the blur kernel as the kernel of different pixels are different to each other. In this paper, the blurred image is considered as a weighed summation of all possible poses, and we proposed to use a PSO (particle swarm optimization) to optimize the weighed parameters of the corresponding poses after building the motion model of the camera. The main issue of using a PSO for deblurring is that it is generally impossible to obtain the ground true of the observed blurred image, which must be used as the input of the PSO algorithm. To solve this problem, firstly a novel image prediction method is proposed which combines a shock filter and a non-linear structure tensor with anisotropic diffusion. The main advantage of the proposed prediction method is that the deblurring process is not misled by rich texture in the image. Secondly an alternatively optimizing procedure is used to gradually refine the motion kernel and the latent image. Experimental results show that our approach makes it possible to model and remove non-uniform motion blur without hardware support.