Multimodal Fusion via a Series of Transfers for Noise Removal

TL;DR

This paper introduces a novel transfer-based multimodal fusion method that effectively removes noise from near-infrared images by addressing edge and brightness discrepancies with contrast, detail, and color transfers, outperforming existing techniques.

Contribution

The paper proposes a new transfer-based fusion approach that handles multimodal discrepancies and enhances noise removal and detail preservation in near-infrared imaging.

Findings

Outperforms conventional fusion and guided filtering methods.

Successfully removes noise and preserves edges and textures.

Achieves superior results compared to state-of-the-art fusion techniques.

Abstract

Near-infrared imaging has been considered as a solution to provide high quality photographs in dim lighting conditions. This imaging system captures two types of multimodal images: one is near-infrared gray image (NGI) and the other is the visible color image (VCI). NGI is noise-free but it is grayscale, whereas the VCI has colors but it contains noise. Moreover, there exist serious edge and brightness discrepancies between NGI and VCI. To deal with this problem, a new transfer-based fusion method is proposed for noise removal. Different from conventional fusion approaches, the proposed method conducts a series of transfers: contrast, detail, and color transfers. First, the proposed contrast and detail transfers aim at solving the serious discrepancy problem, thereby creating a new noise-free and detail-preserving NGI. Second, the proposed color transfer models the unknown colors from the denoised VCI via a linear transform, and then transfers natural-looking colors into the newly generated NGI. Experimental results show that the proposed transfer-based fusion method is highly successful in solving the discrepancy problem, thereby describing edges and textures clearly as well as removing noise completely on the fused images. Most of all, the proposed method is superior to conventional fusion methods and guided filtering, and even the state-of-the-art fusion methods based on scale map and layer decomposition.