@InProceedings{6952662,
  author = {F. Abboud and E. Chouzenoux and J. -. Pesquet and J. -. Chenot and L. Laborelli},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {A hybrid alternating proximal method for blind video restoration},
  year = {2014},
  pages = {1811-1815},
  abstract = {Old analog television sequences suffer from a number of degradations. Some of them can be modeled through convolution with a kernel and an additive noise term. In this work, we propose a new blind deconvolution algorithm for the restoration of such sequences based on a variational formulation of the problem. Our method accounts for motion between frames, while enforcing some level of temporal continuity through the use of a novel penalty function involving optical flow operators, in addition to an edge-preserving regularization. The optimization process is performed by a proximal alternating minimization scheme benefiting from theoretical convergence guarantees. Simulation results on synthetic and real video sequences confirm the effectiveness of our method.},
  keywords = {deconvolution;image restoration;image sequences;hybrid alternating proximal method;blind video restoration;blind deconvolution algorithm;temporal continuity;penalty function;optical flow operators;edge-preserving regularization;video sequences;Kernel;Image restoration;Minimization;Deconvolution;Video sequences;Convergence;Optimization;Blind deconvolution;video processing;optical flow;proximal algorithms;convex optimization;regularization},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925381.pdf},
}

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Some of them can be modeled through convolution with a kernel and an additive noise term. In this work, we propose a new blind deconvolution algorithm for the restoration of such sequences based on a variational formulation of the problem. Our method accounts for motion between frames, while enforcing some level of temporal continuity through the use of a novel penalty function involving optical flow operators, in addition to an edge-preserving regularization. The optimization process is performed by a proximal alternating minimization scheme benefiting from theoretical convergence guarantees. Simulation results on synthetic and real video sequences confirm the effectiveness of our method.","keywords":"deconvolution;image restoration;image sequences;hybrid alternating proximal method;blind video restoration;blind deconvolution algorithm;temporal continuity;penalty function;optical flow operators;edge-preserving regularization;video sequences;Kernel;Image restoration;Minimization;Deconvolution;Video sequences;Convergence;Optimization;Blind deconvolution;video processing;optical flow;proximal algorithms;convex optimization;regularization","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925381.pdf","bibtex":"@InProceedings{6952662,\n author = {F. Abboud and E. Chouzenoux and J. -. Pesquet and J. -. Chenot and L. 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