RIF-Fuse: Invertible Frequency Decomposition with Residual Enhancement for Robust Multimodal Fusion.
Saved in:
| Title: | RIF-Fuse: Invertible Frequency Decomposition with Residual Enhancement for Robust Multimodal Fusion. |
|---|---|
| Authors: | Yang, Anke1 (AUTHOR), Liu, Bingqi1,2 (AUTHOR) liubingqi@cdut.edu.cn, Liu, Mingzhe1,3 (AUTHOR), Ding, Haihua1,3 (AUTHOR), Mo, Peijun1,2 (AUTHOR), Zhao, Chengqiang1,3 (AUTHOR), Liu, Xianghe1 (AUTHOR), Ye, Tao1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1520. 24p. |
| Subjects: | Image fusion, Wavelet transforms, Discrete wavelet transforms, Image enhancement (Imaging systems), Multisensor data fusion |
| Abstract: | Highlights: What are the main findings? A residual-enhanced invertible frequency-domain fusion framework, termed RIF-Fuse, is proposed to explicitly decouple low-frequency structures and high-frequency details for infrared–visible image fusion. A Haar residual enhancement pathway is introduced to compensate for weak high-frequency responses, improving texture preservation and reducing detail suppression during training. A band-aware differential fusion strategy is designed to suppress low-frequency structural conflicts while enhancing high-frequency edges and textures, leading to sharper and more natural fused images on the TNO and RoadScene datasets. What are the implications of the main findings? This study shows that explicit frequency-domain decomposition can provide a more controllable and stable alternative to implicit end-to-end feature fusion for multimodal image synthesis. By jointly improving structural consistency, texture fidelity, and cross-scene robustness, the proposed method provides higher-quality fused images for practical tasks, such as nighttime perception and surveillance. Infrared–visible image fusion (IVIF) seeks to combine the thermal saliency of infrared images with the rich textures of visible images in a single representation. This study proposes RIF-Fuse, a framework designed to enhance fusion stability and detail fidelity through a band-controllable structure–detail decoupling mechanism. We utilize a wavelet-based pipeline to explicitly separate low-frequency structural components from high-frequency textures. A Haar residual enhancement path is integrated into the high-frequency branch to provide low-loss compensation for weak textures, while a band-aware differential fusion strategy is designed to suppress structural conflicts and accentuate edges at the subband level. A two-stage training scheme is further applied to ensure optimization stability. Extensive experiments on the TNO and RoadScene datasets demonstrate that RIF-Fuse produces sharper details and more natural structures compared to state-of-the-art methods. The results indicate that RIF-Fuse achieves a superior balance across multiple objective metrics, offering a robust solution for high-fidelity multimodal image synthesis. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A residual-enhanced invertible frequency-domain fusion framework, termed RIF-Fuse, is proposed to explicitly decouple low-frequency structures and high-frequency details for infrared–visible image fusion. A Haar residual enhancement pathway is introduced to compensate for weak high-frequency responses, improving texture preservation and reducing detail suppression during training. A band-aware differential fusion strategy is designed to suppress low-frequency structural conflicts while enhancing high-frequency edges and textures, leading to sharper and more natural fused images on the TNO and RoadScene datasets. What are the implications of the main findings? This study shows that explicit frequency-domain decomposition can provide a more controllable and stable alternative to implicit end-to-end feature fusion for multimodal image synthesis. By jointly improving structural consistency, texture fidelity, and cross-scene robustness, the proposed method provides higher-quality fused images for practical tasks, such as nighttime perception and surveillance. Infrared–visible image fusion (IVIF) seeks to combine the thermal saliency of infrared images with the rich textures of visible images in a single representation. This study proposes RIF-Fuse, a framework designed to enhance fusion stability and detail fidelity through a band-controllable structure–detail decoupling mechanism. We utilize a wavelet-based pipeline to explicitly separate low-frequency structural components from high-frequency textures. A Haar residual enhancement path is integrated into the high-frequency branch to provide low-loss compensation for weak textures, while a band-aware differential fusion strategy is designed to suppress structural conflicts and accentuate edges at the subband level. A two-stage training scheme is further applied to ensure optimization stability. Extensive experiments on the TNO and RoadScene datasets demonstrate that RIF-Fuse produces sharper details and more natural structures compared to state-of-the-art methods. The results indicate that RIF-Fuse achieves a superior balance across multiple objective metrics, offering a robust solution for high-fidelity multimodal image synthesis. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101520 |
