Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising

• URL: http://arxiv.org/abs/2412.16645v1
• Date: Sat, 21 Dec 2024 14:31:36 GMT
• Title: Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising
• Authors: Yuchen Wang, Hongyuan Wang, Lizhi Wang, Xin Wang, Lin Zhu, Wanxuan Lu, Hua Huang,
• Abstract summary: We develop a cross-field Frequency Correlation Exploiting Network (FCENet) for NIR-assisted image denoising. We first propose the frequency correlation prior based on an in-depth statistical frequency analysis of NIR-RGB image pairs. We then establish a frequency learning framework composed of Frequency Dynamic Selection Mechanism (FDSM) and Frequency Exhaustive Fusion Mechanism (FEFM)
• Score: 27.54777017705553
• License:
• Abstract: Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still struggle to balance the contributions of two fields in the process of image fusion. In response to this, in this paper, we develop a cross-field Frequency Correlation Exploiting Network (FCENet) for NIR-assisted image denoising. We first propose the frequency correlation prior based on an in-depth statistical frequency analysis of NIR-RGB image pairs. The prior reveals the complementary correlation of NIR and RGB images in the frequency domain. Leveraging frequency correlation prior, we then establish a frequency learning framework composed of Frequency Dynamic Selection Mechanism (FDSM) and Frequency Exhaustive Fusion Mechanism (FEFM). FDSM dynamically selects complementary information from NIR and RGB images in the frequency domain, and FEFM strengthens the control of common and differential features during the fusion of NIR and RGB features. Extensive experiments on simulated and real data validate that our method outperforms various state-of-the-art methods in terms of image quality and computational efficiency. The code will be released to the public.

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