Title: Efficient Rectified Flow for Image Fusion
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Fusion

Multi-modality Image Fusion

Infrared and Visible Image Fusion
Abstract: Image fusion is a fundamental and important task in computer vision, aiming to combine complementary information from different modalities to fuse images. In recent years, diffusion models have made significant developments in the field of image fusion. However, diffusion models often require complex computations and redundant inference time, which reduces the applicability of these methods. To address this issue, we propose RFfusion, an efficient one-step diffusion model for image fusion based on Rectified Flow. We incorporate Rectified Flow into the image fusion task to straighten the sampling path in the diffusion model, achieving one-step sampling without the need for additional training, while still maintaining high-quality fusion results. Furthermore, we propose a task-specific variational autoencoder (VAE) architecture tailored for image fusion, where the fusion operation is embedded within the latent space to further reduce computational complexity. To address the inherent discrepancy between conventional reconstruction-oriented VAE objectives and the requirements of image fusion, we introduce a two-stage training strategy. This approach facilitates the effective learning and integration of complementary information from multi-modal source images, thereby enabling the model to retain fine-grained structural details while significantly enhancing inference efficiency. Extensive experiments demonstrate that our method outperforms other state-of-the-art methods in terms of both inference speed and fusion quality.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes RFfusion, an efficient one-step image fusion framework that integrates Rectified Flow into multimodal fusion tasks. By combining a task-specific latent-space VAE and a two-stage training strategy, the method achieves fast inference and high-quality results across infrared-visible, multi-exposure, and multi-focus fusion benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1.Integrating Rectified Flow into multimodal image fusion is interesting and technically reasonable. \n2.The method shows consistently strong performance across multiple fusion tasks.\n3.The paper is well-organized, with clear methodology, and solid experiments.\n4.The proposed two-stage latent space training strategy is practically appealing.\n\nWeaknesses:\n1.The method does not incorporate any explicit mechanisms to model modality-specific characteristics, such as dual-branch encoders or cross-modality attention. Would this design limit the model’s ability to preserve complementary features from each modality?\n2.The VAE encoder exclusively takes the visible image as input, while the infrared modality is introduced only via posterior gradients during sampling. I am curious whether may result in biased latent representations and insufficient encoding of infrared-specific semantics.\n3.Equation (10) introduces a posterior regularization term involving a mapping function, yet the formulation and implementation of M are not clearly specified. It is unclear whether this component is learned, fixed, or task-specific, making the role of the fusion prior difficult to assess.\n4.In Equation (13), the fusion velocity is defined as a sum of the pre-trained rectified flow and a posterior gradient term. However, the dynamics of their interaction are not analyzed. It remains unclear whether this additive formulation can consistently preserve modality alignment or may distort flow trajectories under large posterior corrections.\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article proposes an efficient one-step image fusion method RFfusion, which combines Rectified Flow and task specific VAE structure, aiming to improve the inference efficiency of image fusion while maintaining high-quality output.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The RFFusion proposed by the author is the first to apply Rectified Flow to image fusion tasks, proposing a one-step sampling mechanism that significantly reduces inference time. \n\n2. A two-stage VAE training strategy is designed for image fusion, effectively solving the problem of inconsistent reconstruction and fusion targets.\n\n3. Quantitative and qualitative comparisons of various image fusion tasks are conducted (infrared visible light, multi exposure, multi focus) and the method performs excellently.\n\n\n\nWeakness:\n1. The pre trained Rectified Flow model is not specifically designed for image fusion, which limits further performance improvement.\n\n2. The author did not conduct ablation experiments based on whether Rectified Flow and VAE structure were used, which limited the evaluation of the improvement points.\n\nQUESTIONS:\n1. Although experiments have shown that fine-tuning is not necessary in multiple task, could generalization tests (such as MRI-PET mentioned in Section 1 Introduction) be provided under different modal combinations for further validation?\n\n2. Although the effectiveness of the loss term has been demonstrated, could authors further ablate the model architecture (such as different encoder or decoder structures in the proposed VAE module architecture, and the detailed architecture design in the proposed fusion prior guidance)?\n\n\n3. Authors currently use visible image as the starting point instead of the other modality or the pure noise. Could authors consideration been given to using other starting point (such as dual modal images) to improve fusion consistency?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes RFfusion, an efficient image fusion method based on rectified flow. RFfusion leverages rectified flow to achieve one-step sampling for image fusion. In addition, it incorporates a variational autoencoder (VAE) to accelerate the fusion process and introduces a two-stage training strategy specifically designed to adapt to the image fusion task. These components work together to significantly enhance both the speed and quality of the fusion results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) Compared to previous methods, the author's proposed approach significantly accelerates image fusion speed and achieves leading performance across multiple tasks.\n2) The authors introduced rectified flow and VAE into the image fusion task, achieving promising results and providing inspiration for subsequent research.\n3) The authors highlight that their method is adaptable to various fusion tasks and can be transferred to other diffusion-based image fusion approaches, demonstrating exciting generalization potential.\n\nWeaknesses:\n1) The authors validate their method exclusively using the baseline DDFM approach. Given that other approaches, such as CCF, also utilize similar image fusion strategies, it would strengthen the paper if the authors could demonstrate the effectiveness of incorporating rectified flow and VAE into these alternative methods to better establish the generalizability of their proposed technique.\n2) The authors did not provide performance evaluations of their method on additional image fusion tasks or datasets, such as medical image fusion, thus failing to comprehensively demonstrate the superiority of their approach in broader scenarios.\n3) The authors primarily explain why their method can accelerate the original approach; however, according to the results presented in the paper, the proposed method also surpasses DDFM in quantitative metrics. The authors have not provided a detailed explanation for the observed performance improvements.\n\nQUESTIONS:\n1) The authors mentioned using a pretrained model from rectified flow in their paper. Could the authors provide detailed information about the specifics of this pretrained model?\n2) Could the authors provide the performance of the proposed method on additional datasets and other fusion tasks to better demonstrate its generalization capability?\n3) The authors employed additional loss functions during VAE training, as shown in Eq. (17). Could the authors explain the rationale behind choosing these specific losses to finetune the VAE?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method that leverages Rectified Flow for latent space sampling to accelerate image fusion inference. It introduces VAE to bring the image fusion process into the latent space and employs a two-stage finetuning strategy to adapt the VAE prior to the fusion task, further enhancing inference speed.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) RFfusion successfully addresses the inference time issues of previous diffusion-based methods by significantly accelerating the inference process while maintaining impressive fusion performance.\n2) The paper is generally well-structured and coherent, with smooth writing and clear, visually appealing figures.\n\nWeaknesses:\n1) Although RFfusion effectively improves inference speed, the quantitative fusion performance presented in the paper does not appear to show a definitive advantage over other methods.\n2) RFfusion is only built upon DDFM and has not been validated on other diffusion-based methods, making it difficult to substantiate the generalization capability claimed in the paper.\n3) Although RFfusion employs both VAE and Rectified Flow to accelerate inference, the paper lacks ablation studies to separately validate the individual contributions of VAE and Rectified Flow to the overall speed improvement.\n\nQUESTIONS:\n1) The paper mentions that using visible images as the starting point for sampling better results than using Gaussian noise. Please explain the reason behind this choice.\n2) In the first stage of VAE training, the authors used the LLVIP and MSRS datasets, while in the second stage only the MSRS dataset was used. Please explain the rationale behind this setup.\n3) Please elaborate on the details of the VAE training process, such as the computational cost of the two-stage VAE training, and which type of pre-trained VAE prior is used for finetuning.\n\nLIMITATIONS:\nsee weakness", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}