Learning deep optimizer for blind image deconvolution
2019 ◽
Vol 17
(06)
◽
pp. 1950044
Keyword(s):
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 unstable. 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 applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our network achieves competitive performance on GoPro dataset, and our model is extremely lightweight compared with the state-of-the-art works.
2017 ◽
Vol E100.A
(3)
◽
pp. 846-853
2006 ◽
Vol 16
(2)
◽
pp. 137-148
◽
2018 ◽
Vol 336
◽
pp. 425-438
Keyword(s):