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A Deep Optimization Approach for Image Deconvolution
[article]
2019
arXiv
pre-print
In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be instable, or ineffective. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently
arXiv:1904.07516v1
fatcat:oswvicolezeffmioce4sc7fjfe