DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared
  Image Destriping
Shiqi Yang Hanlin Qin Shuai Yuan Xiang Yan Hossein Rahmani
CycleGAN has been proven to be an advanced approach for unsupervised image
restoration. This framework consists of two generators: a denoising one for
inference and an auxiliary one for modeling noise to fulfill cycle-consistency
constraints. However, when applied to the infrared destriping task, it becomes
challenging for the vanilla auxiliary generator to consistently produce
vertical noise under unsupervised constraints. This poses a threat to the
effectiveness of the cycle-consistency loss, leading to stripe noise residual
in the denoised image. To address the above issue, we present a novel framework
for single-frame infrared image destriping, named DestripeCycleGAN. In this
model, the conventional auxiliary generator is replaced with a priori stripe
generation model (SGM) to introduce vertical stripe noise in the clean data,
and the gradient map is employed to re-establish cycle-consistency. Meanwhile,
a Haar wavelet background guidance module (HBGM) has been designed to minimize
the divergence of background details between the different domains. To preserve
vertical edges, a multi-level wavelet U-Net (MWUNet) is proposed as the
denoising generator, which utilizes the Haar wavelet transform as the sampler
to decline directional information loss. Moreover, it incorporates the group
fusion block (GFB) into skip connections to fuse the multi-scale features and
build the context of long-distance dependencies. Extensive experiments on real
and synthetic data demonstrate that our DestripeCycleGAN surpasses the
state-of-the-art methods in terms of visual quality and quantitative
evaluation. Our code will be made public at
https://github.com/0wuji/DestripeCycleGAN.