  Image deblurring task is an ill-posed one, where exists infinite feasible
solutions for blurry image. Modern deep learning approaches usually discard the
learning of blur kernels and directly employ end-to-end supervised learning.
Popular deblurring datasets define the label as one of the feasible solutions.
However, we argue that it's not reasonable to specify a label directly,
especially when the label is sampled from a random distribution. Therefore, we
propose to make the network learn the distribution of feasible solutions, and
design based on this consideration a novel multi-head output architecture and
corresponding loss function for distribution learning. Our approach enables the
model to output multiple feasible solutions to approximate the target
distribution. We further propose a novel parameter multiplexing method that
reduces the number of parameters and computational effort while improving
performance. We evaluated our approach on multiple image-deblur models,
including the current state-of-the-art NAFNet. The improvement of best overall
(pick the highest score among multiple heads for each validation image) PSNR
outperforms the compared baselines up to 0.11~0.18dB. The improvement of the
best single head (pick the best-performed head among multiple heads on
validation set) PSNR outperforms the compared baselines up to 0.04~0.08dB. The
codes are available at https://github.com/Liu-SD/multi-output-deblur.
