ARCNet: An Asymmetric Residual Wavelet Column Correction Network for
  Infrared Image Destriping
Shuai Yuan Hanlin Qin Xiang Yan Naveed Akhtar Shiqi Yang Shuowen Yang
Infrared image destriping seeks to restore high-quality content from degraded
images. Recent works mainly address this task by leveraging prior knowledge to
separate stripe noise from the degraded image. However, constructing a robust
decoupling model for that purpose remains challenging, especially when
significant similarities exist between the stripe noise and vertical background
structure. Addressing that, we introduce Asymmetric Residual wavelet Column
correction Network (ARCNet) for image destriping, aiming to consistently
preserve spatially precise high-resolution representations. Our neural model
leverages a novel downsampler, residual haar discrete wavelet transform
(RHDWT), stripe directional prior knowledge and data-driven learning to induce
a model with enriched feature representation of stripe noise and background. In
our technique, the inverse wavelet transform is replaced by transposed
convolution for feature upsampling, which can suppress noise crosstalk and
encourage the network to focus on robust image reconstruction. After each
sampling, a proposed column non-uniformity correction module (CNCM) is
leveraged by our method to enhance column uniformity, spatial correlation, and
global self-dependence between each layer component. CNCM can establish
structural characteristics of stripe noise and utilize contextual information
at long-range dependencies to distinguish stripes with varying intensities and
distributions. Extensive experiments on synthetic data, real data, and infrared
small target detection tasks show that the proposed method outperforms
state-of-the-art single-image destriping methods both visually and
quantitatively by a considerable margin. Our code will be made publicly
available at \url{https://github.com/xdFai}.