ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping

• URL: http://arxiv.org/abs/2401.15578v2
• Date: Tue, 4 Jun 2024 11:47:15 GMT
• Title: ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
• Authors: Shuai Yuan, Hanlin Qin, Xiang Yan, Shiqi Yang, Shuowen Yang, Naveed Akhtar,
• Abstract summary: We propose a novel infrared image destriping method called Asymmetric Sampling Correction Network (ASCNet) Our ASCNet consists of three core elements: Residual Haar Discrete Wavelet Transform (RHDWT), Pixel Shuffle (PS), and Column Non-uniformity Correction Module (CNCM)
• Score: 26.460122241870696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a novel infrared image destriping method, called Asymmetric Sampling Correction Network (ASCNet), that can effectively capture global column relationships and embed them into a U-shaped framework, providing comprehensive discriminative representation and seamless semantic connectivity. Our ASCNet consists of three core elements: Residual Haar Discrete Wavelet Transform (RHDWT), Pixel Shuffle (PS), and Column Non-uniformity Correction Module (CNCM). Specifically, RHDWT is a novel downsampler that employs double-branch modeling to effectively integrate stripe-directional prior knowledge and data-driven semantic interaction to enrich the feature representation. Observing the semantic patterns crosstalk of stripe noise, PS is introduced as an upsampler to prevent excessive apriori decoding and performing semantic-bias-free image reconstruction. After each sampling, CNCM captures the column relationships in long-range dependencies. By incorporating column, spatial, and self-dependence information, CNCM well establishes a global context to distinguish stripes from the scene's vertical structures. Extensive experiments on synthetic data, real data, and infrared small target detection tasks demonstrate that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively. Our code will be made publicly available at https://github.com/xdFai/ASCNet.

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