【摘 要】
:
In this talk,I will show that many iterative optimization algorithms in image processing and analysis can be modeled as deep networks,by which the parameter
【机 构】
:
XianJiaotongUniversity
【出 处】
:
2016信号处理、优化与控制国际研讨会(2016 International Workshop on Signal Pr
论文部分内容阅读
In this talk,I will show that many iterative optimization algorithms in image processing and analysis can be modeled as deep networks,by which the parameters in energy function and the corresponding optimization algorithm can be discriminatively learned for specific task.I will show that the iterative shrinkage in signal processing,ADMM(Alternating Direction Method of Multipliers)algorithm optimizing a compressive sensing model,a multi-atlas organ segmentation method in medical image analysis can be formulated as deep networks.These networks are non-conventional,task-specific and achieved state-of-the-art results in image restoration,compressive sensing MRI and cardiac MR image segmentation.
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