Tensor Voting for Image Correction by Global and Local Intensity Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced Biggs---Andrews Asymmetric Iterative Blind Deconvolution
Multidimensional Systems and Signal Processing
Coded exposure photography: motion deblurring using fluttered shutter
ACM SIGGRAPH 2006 Papers
Constrained iterations for blind deconvolution and convexity issues
Journal of Computational and Applied Mathematics
Spatially adaptive intensity bounds for image restoration
EURASIP Journal on Applied Signal Processing
A nonparametric procedure for blind image deblurring
Computational Statistics & Data Analysis
Out-of-focus Blur estimation for blind image deconvolution: using particle swarm optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Journal of Biomedical Imaging
Blind identification and deconvolution for noisy two-input two-output channels
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Wavelet transform based gaussian point spread function estimation
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Combining iterative inverse filter with shock filter for baggage inspection image deblurring
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Blind extraction of singularly mixed source signals
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Total variation blind deconvolution employing split Bregman iteration
Journal of Visual Communication and Image Representation
Joint MAP estimation for blind deconvolution: when does it work?
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
ABC optimized neural network model for image deblurring with its FPGA implementation
Microprocessors & Microsystems
Blind image deconvolution using a banded matrix method
Numerical Algorithms
Efficient blind image deconvolution using spectral non-Gaussianity
Integrated Computer-Aided Engineering
Hi-index | 35.68 |
We present a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function. The technique applies to situations in which the scene consists of a finite support object against a uniformly black, grey, or white background. This occurs in certain types of astronomical imaging, medical imaging, and one-dimensional (1-D) gamma ray spectra processing, among others. The only information required are the nonnegativity of the true image and the support size of the original object. The restoration procedure involves recursive filtering of the blurred image to minimize a convex cost function. We prove convexity of the cost function, establish sufficient conditions to guarantee a unique solution, and examine the performance of the technique in the presence of noise. The new approach is experimentally shown to be more reliable and to have faster convergence than existing nonparametric finite support blind deconvolution methods. For situations in which the exact object support is unknown, we propose a novel support-finding algorithm