Wavelet Deep Neural Network for Stripe Noise Removal

Abstract

The stripe noise effects severely degrade the image quality in infrared imaging systems. The existing destriping algorithms still struggle to balance noise suppression, detail preservation, and real-time performance, which retards their application in spectral imaging and signal processing field. To solve this problem, an innovative wavelet deep neural network from the perspective of transform domain is presented in this paper, which takes the intrinsic characteristics of stripe noise and complementary information between the coefficients of different wavelet sub-bands into full consideration to accurately estimate the noise with the lower computational load. In addition, a special directional regularizer is further defined to separate the scene details from stripe noise more thoroughly and recover the details more accurately. The extensive experiments on simulated and real data demonstrate that our proposed method outperforms several classical destriping methods on both quantitative and qualitative assessments.