DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping

• URL: http://arxiv.org/abs/2402.09101v1
• Date: Wed, 14 Feb 2024 11:22:20 GMT
• Title: DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping
• Authors: Shiqi Yang, Hanlin Qin, Shuai Yuan, Xiang Yan, Hossein Rahmani
• Abstract summary: CycleGAN has been proven to be an advanced approach for unsupervised image restoration. We present a novel framework for single-frame infrared image destriping, named DestripeCycleGAN.
• Score: 15.797480466799222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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