A Deep Optimization Approach for Image Deconvolution [article]

Zhijian Luo, Siyu Chen, Yuntao Qian
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
more » ... 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-art works.
arXiv:1904.07516v1 fatcat:oswvicolezeffmioce4sc7fjfe