  Recent blind super-resolution (SR) methods typically consist of two branches,
one for degradation prediction and the other for conditional restoration.
However, our experiments show that a one-branch network can achieve comparable
performance to the two-branch scheme. Then we wonder: how can one-branch
networks automatically learn to distinguish degradations? To find the answer,
we propose a new diagnostic tool -- Filter Attribution method based on Integral
Gradient (FAIG). Unlike previous integral gradient methods, our FAIG aims at
finding the most discriminative filters instead of input pixels/features for
degradation removal in blind SR networks. With the discovered filters, we
further develop a simple yet effective method to predict the degradation of an
input image. Based on FAIG, we show that, in one-branch blind SR networks, 1)
we are able to find a very small number of (1%) discriminative filters for each
specific degradation; 2) The weights, locations and connections of the
discovered filters are all important to determine the specific network
function. 3) The task of degradation prediction can be implicitly realized by
these discriminative filters without explicit supervised learning. Our findings
can not only help us better understand network behaviors inside one-branch
blind SR networks, but also provide guidance on designing more efficient
architectures and diagnosing networks for blind SR.
