  There have been many discriminative learning methods using convolutional
neural networks (CNN) for several image restoration problems, which learn the
mapping function from a degraded input to the clean output. In this letter, we
propose a self-committee method that can find enhanced restoration results from
the multiple trial of a trained CNN with different but related inputs.
Specifically, it is noted that the CNN sometimes finds different mapping
functions when the input is transformed by a reversible transform and thus
produces different but related outputs with the original. Hence averaging the
outputs for several different transformed inputs can enhance the results as
evidenced by the network committee methods. Unlike the conventional committee
approaches that require several networks, the proposed method needs only a
single network. Experimental results show that adding an additional transform
as a committee always brings additional gain on image denoising and single
image supre-resolution problems.
