Fast Bayesian Uncertainty Estimation of Batch Normalized Single Image Super-Resolution Network
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In recent years, deep convolutional neural network (CNN) has achieved unprecedented success in image super-resolution (SR) task. But the black-box nature of the neural network and due to its lack of transparency, it is hard to trust the outcome. In this regards, we introduce a Bayesian approach for uncertainty estimation in super-resolution network. We generate Monte Carlo (MC) samples from a posterior distribution by using batch mean and variance as a stochastic parameter in the batch-normalization layer during test time. Those MC samples not only reconstruct the image from its low-resolution counterpart but also provides a confidence map of reconstruction which will be very impactful for practical use. We also introduce a faster approach for estimating the uncertainty, and it can be useful for real-time applications. We validate our results using standard datasets for performance analysis and also for different domain-specific super-resolution task. We also estimate uncertainty quality using standard statistical metrics and also provides a qualitative evaluation of uncertainty for SR applications.
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