  Recently, CycleGAN was shown to provide high-performance, ultra-fast
denoising for low-dose X-ray computed tomography (CT) without the need for a
paired training dataset. Although this was possible thanks to cycle
consistency, CycleGAN requires two generators and two discriminators to enforce
cycle consistency, demanding significant GPU resources and technical skills for
training. A recent proposal of tunable CycleGAN with Adaptive Instance
Normalization (AdaIN) alleviates the problem in part by using a single
generator. However, two discriminators and an additional AdaIN code generator
are still required for training. To solve this problem, here we present a novel
cycle-free Cycle-GAN architecture, which consists of a single generator and a
discriminator but still guarantees cycle consistency. The main innovation comes
from the observation that the use of an invertible generator automatically
fulfills the cycle consistency condition and eliminates the additional
discriminator in the CycleGAN formulation. To make the invertible generator
more effective, our network is implemented in the wavelet residual domain.
Extensive experiments using various levels of low-dose CT images confirm that
our method can significantly improve denoising performance using only 10% of
learnable parameters and faster training time compared to the conventional
CycleGAN.
