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

TL;DR

This paper introduces FCENet, a novel network that exploits frequency domain correlations between NIR and RGB images to improve image denoising, effectively balancing contributions from both modalities.

Contribution

The paper proposes a frequency correlation prior and a framework with dynamic selection and exhaustive fusion mechanisms for NIR-RGB image denoising.

Findings

Outperforms state-of-the-art denoising methods on simulated data.

Effectively leverages frequency domain correlations for improved detail restoration.

Validated on real-world noisy images with superior results.

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 process of NIR and RGB features. Extensive experiments on simulated and real data validate that the proposed method outperforms other state-of-the-art methods. The code will be released at https://github.com/yuchenwang815/FCENet.