Multi-modal image matching is a fundamental task in multi-view and multi-modal image processing. Its key challenge lies in extracting features that remain consistent despite drastic appearance variations across modalities. However, the learning of the feature is hindered by the scarcity and the inaccurate alignment of existing multi-modal datasets. To address this, we propose a knowledge distillation framework termed SGPFeat that transfers rich prior knowledge from large-scale unimodal tasks to enhance multi-modal representation learning. Specifically, semantic priors from a vision foundation model guide the feature extractor to identify shared semantic structures across modalities, enabling better generalization under large appearance gaps. In parallel, geometric priors derived from accurately aligned visible-light datasets improve detection precision on noisy aligned multi-modal pairs. Furthermore, we introduce a Heterogeneous Feature Aggregation (HFA) module to facilitate effective distillation and feature representation. Extensive experiments demonstrate that semantic and geometric priors bring significant improvement for our SGPFeat across diverse multi-modal image matching benchmarks.

Recent advances in naturalistic physical adversarial patch generation show great promise in protecting personal privacy against detector-based malicious surveillance while remaining inconspicuous to human observers. In this work, we present the first systematic categorization and in-depth re-examination of existing methods into three representative paradigms, revealing a pervasive imbalance: enforcing naturalness constraints inherently restricts the adversarial search space, thus limiting attack performance. To address this challenge, we propose a novel paradigm based on class-optimized diffusion, termed Diff-NAT. Diff-NAT leverages pretrained diffusion models as powerful natural image priors and introduces a unified iterative framework that jointly optimizes two complementary components: semantic-level textual prompts and instance-level latent codes. Specifically, prompt optimization enables broad traversal across inter-class semantic regions, while latent refinement allows for fine-grained manipulation within class objectives. This dual-level optimization facilitates progressive navigation toward adversarial distributions embedded within the natural semantic manifold. Extensive experiments in both digital and physical settings demonstrate that Diff-NAT outperforms existing SOTA approaches in terms of both visual realism and aggressiveness.

Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics. To address this challenge, we propose a robust fusion controller (RFC) capable of achieving degradation-aware image fusion through fine-grained language instructions, ensuring its reliable application in adverse environments. Specifically, RFC first parses language instructions to innovatively derive the functional condition and the spatial condition, where the former specifies the degradation type to remove, while the latter defines its spatial coverage. Then, a composite control priori is generated through a multi-condition coupling network, achieving a seamless transition from abstract language instructions to latent control variables. Subsequently, we design a hybrid attention-based fusion network to aggregate multi-modal information, in which the obtained composite control priori is deeply embedded to linearly modulate the intermediate fused features. To ensure the alignment between language instructions and control outcomes, we introduce a novel language-feature alignment loss, which constrains the consistency between feature-level gains and the composite control priori. Extensive experiments on publicly available datasets demonstrate that our RFC is robust against various composite degradations, particularly in highly challenging flare scenarios.

Two-view correspondence learning aims to discern true and false correspondences between image pairs by recognizing their underlying different information. Previous methods either treat the information equally or require the explicit storage of the entire context, tending to be laborious in real-world scenarios. Inspired by Mamba's inherent selectivity, we propose CorrMamba, a Correspondence filter leveraging Mamba's ability to selectively mine information from true correspondences while mitigating interference from false ones, thus achieving adaptive focus at a lower cost. To prevent Mamba from being potentially impacted by unordered keypoints that obscured its ability to mine spatial information, we customize a causal sequential learning approach based on the Gumbel-Softmax technique to establish causal dependencies between features in a fully autonomous and differentiable manner. Additionally, a local-context enhancement module is designed to capture critical contextual cues essential for correspondence pruning, complementing the core framework. Extensive experiments on relative pose estimation, visual localization, and analysis demonstrate that CorrMamba achieves state-of-the-art performance. Notably, in outdoor relative pose estimation, our method surpasses the previous SOTA by 2.58 absolute percentage points in AUC@20°, highlighting its practical superiority. Our code is publicly available at https://github.com/ShineFox/CorrMamba.

Current research on adversarial attacks mainly focuses on RGB trackers, with no existing methods for attacking RGB-T cross-modal trackers. To fill this gap and overcome its challenges, we propose a progressive adversarial patch generation framework and achieve cross-modal stealth. On the one hand, we design a coarse-to-fine architecture grounded in the latent space to progressively and precisely uncover the vulnerabilities of RGB-T trackers. On the other hand, we introduce a correlation-breaking loss that disrupts the modal coupling within trackers, spanning from the pixel to the semantic level. These two design elements ensure that the proposed method can overcome the obstacles posed by cross-modal information complementarity in implementing attacks. Furthermore, to enhance the reliable application of the adversarial patches in real world, we develop a point tracking-based reprojection strategy that effectively mitigates performance degradation caused by multi-angle distortion during imaging. Extensive experiments demonstrate the superiority of our method.

The research on adversarial attacks against trackers primarily concentrates on the RGB modality, whereas the methodology for attacking RGB-T multi-modal trackers has seldom been explored so far. This work represents an innovative attempt to develop an adaptive cross attack framework via multi-modal response decoupling, generating multi-modal adversarial patches to evade RGB-T trackers. Specifically, a modal-aware adaptive attack strategy is introduced to weaken the modality with high common information contribution alternately and iteratively, achieving the modal decoupling attack. In order to perturb the judgment of the modal balance mechanism in the tracker, we design a modal disturbance loss to increase the distance of the response map of the single-modal adversarial samples in the tracker. Besides, we also propose a novel spatio-temporal joint attack loss to progressively deteriorate the tracker’s perception of the target. Moreover, the design of the shared adversarial shape enables the generated multi-modal adversarial patches to be readily deployed in real-world scenarios, effectively reducing the interference of the patch posting process on the shape attack of the infrared adversarial layer. Extensive digital and physical domain experiments demonstrate the effectiveness of our multi-modal adversarial patch attack. Our code is available at https://github.com/Xinyu-Xiang/ACAttack.

This study proposes PDFuse, a robust, general training-free image fusion framework built on pre-trained latent diffusion models with projection–manifold regularization. By redefining fusion as a diffusion inference process constrained by multiple source images, PDFuse can adapt to varied image modalities and produce high-fidelity outputs utilizing the diffusion prior. To ensure both source consistency and full utilization of generative priors, we develop novel projection–manifold regularization, which consists of two core mechanisms. On the one hand, the Multi-source Information Consistency Projection (MICP) establishes a projection system between diffusion latent representations and source images, solved efficiently via conjugate gradients to inject multi-source information into the inference. On the other hand, the Latent Manifold-preservation Guidance (LMG) aligns the latent distribution of diffusion variables with that of the sources, guiding generation to respect the model’s manifold prior. By alternating these mechanisms, PDFuse strikes an optimal balance between fidelity and generative quality, achieving superior fusion performance across diverse tasks. Moreover, PDFuse constructs a canonical interference operator set. It synergistically incorporates it into the aforementioned dual mechanisms, effectively leveraging generative priors to address various degradation issues during the fusion process without requiring clean data for supervising training. Extensive experimental evidence substantiates that PDFuse achieves highly competitive performance across diverse image fusion tasks. The code is publicly available at https://github.com/Leiii-Cao/PDFuse.

This paper proposes a Retinex-driven reinforced diffusion model for low-light image enhancement, termed Diff-Retinex++, to address various degradations caused by low light. Our main approach integrates the diffusion model with Retinex-driven restoration to achieve physically-inspired generative enhancement, making it a pioneering effort. To be detailed, Diff-Retinex++ consists of two-stage view modules, including the Denoising Diffusion Model (DDM), and the Retinex-Driven Mixture of Experts Model (RMoE). First, DDM treats low-light image enhancement as one type of image generation task, benefiting from the powerful generation ability of diffusion model to handle the enhancement. Second, we design the Retinex theory into the plug-and-play supervision attention module. It leverages the latent features in the backbone and knowledge distillation to learn Retinex rules, and further regulates these latent features through the attention mechanism. In this way, it couples the relationship between Retinex decomposition and image enhancement in a new view, achieving dual improvement. In addition, the Low-Light Mixture of Experts preserves the vividness of the diffusion model and fidelity of the Retinex-driven restoration to the greatest extent. Ultimately, the iteration of DDM and RMoE achieves the goal of Retinex-driven reinforced diffusion model. Extensive experiments conducted on real-world low-light datasets qualitatively and quantitatively demonstrate the effectiveness, superiority, and generalization of the proposed method.

Existing image fusion methods struggle to accommodate composite degradation and do not support users flexibly modulating the semantic objects of interest. To address these challenges, this study proposes a composite degradation-robust image fusion framework with language-driven semantics, called OmniFuse. Firstly, OmniFuse establishes a novel multi-modal information fusion paradigm based on the latent diffusion model (LDM). By projecting the information fusion function into the latent space of the LDM, the information fusion process is seamlessly integrated with the diffusion process. Thus, OmniFuse fully leverages the powerful generative capabilities of LDM to eliminate composite degradation, thereby achieving highly robust image fusion. Secondly, OmniFuse develops a language-driven controllable fusion strategy to strengthen fusion flexibility. It employs a language-driven feature fusion module (LFFM) to receive the specified localization priori, dynamically aggregating multi-modal features. Within LFFM, a visual enhancement regularization is introduced to highlight objects of interest for capturing perceptual attention, while reverse semantic driving is established to strengthen their semantic attributes. Together, the visual and semantic constraints can implicitly correct the imperfect localization priori, further refining the accuracy of language-driven control. Extensive experiments demonstrate the omnipotent performance of OmniFuse, with significant advantages in robustness and flexibility compared to state-of-the-art methods.

Existing deep pan-sharpening methods lack the learning of complementary information between PAN and MS modalities in the intermediate layers, and exhibit low interpretability due to their black-box designs. To this end, an interpretable deep unfolded network with intrinsic supervision for pan-sharpening is proposed. Building upon the observation degradation process, it formulates the pan-sharpening task as a variational model minimization with spatial consistency prior and spectral projection prior. The former prior requires a joint component decomposition of PAN and MS images to extract intrinsic features. By being supervised in the intermediate layers, it can selectively provide high-frequency information for spatial enhancement. The latter prior constrains the intensity correlation between MS and PAN images derived from physical observations, so as to improve spectral fidelity. To further enhance the transparency of network design, we develop an iterative solution algorithm following the half-quadratic splitting to unfold the deep model. It rigorously adheres to the variational model, significantly enhancing the interpretability behind network design and efficiently alternating the optimization of the network. Extensive experiments demonstrate the advantages of our method compared to state-of-the-arts, showcasing its remarkable generalization capability to real-world scenes. Our code is publicly available at https://github.com/Baixuzx7/DISPNet.

This work proposes a robust 3D medical image fusion framework to establish a mutual-reinforcing mechanism between visual fusion and lesion segmentation, achieving their double improvement. Specifically, we explore the consistency between vision and semantics by sharing feature fusion modules. Through the coupled optimization of the visual fusion loss and the lesion segmentation loss, visual-related and semantic-related features will be pulled into the same domain, effectively promoting accuracy improvement in a mutual-reinforcing manner. Further, we establish the robustness guarantees by constructing a two-level refinement constraint in the process of feature extraction and reconstruction. Benefiting from full consideration for common degradations in medical images, our framework can not only provide clear visual fusion results for doctor's observation, but also enhance the defense ability of lesion segmentation against these negatives. Extensive evaluations of visual fusion and lesion segmentation scenarios demonstrate the advantages of our method in terms of accuracy and robustness. Moreover, our proposed framework is generic, which can be well-compatible with existing lesion segmentation algorithms and improve their performance. The code is publicly available at https://github.com/HaoZhang1018/RMR-Fusion.

This paper proposes a coupled learning framework to break the performance bottleneck of infrared-visible image fusion and segmentation, called MRFS. By leveraging the intrinsic consistency between vision and semantics, it emphasizes mutual reinforcement rather than treating these tasks as separate issues. First, we embed weakened information recovery and salient information integration into the image fusion task, employing the CNN-based interactive gated mixed attention (IGM-Att) module to extract high-quality visual features. This aims to satisfy human visual perception, producing fused images with rich textures, high contrast, and vivid colors. Second, a transformer-based progressive cycle attention (PC-Att) module is developed to enhance semantic segmentation. It establishes single-modal self-reinforcement and cross-modal mutual complementarity, enabling more accurate decisions in machine semantic perception. Then, the cascade of IGM-Att and PC-Att couples image fusion and semantic segmentation tasks, implicitly bringing vision-related and semantics-related features into closer alignment. Therefore, they mutually provide learning priors to each other, resulting in visually satisfying fused images and more accurate segmentation decisions. Extensive experiments on public datasets showcase the advantages of our method in terms of visual satisfaction and decision accuracy. The code is publicly available at https://github.com/HaoZhang1018/MRFS.

Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing with degradations in low-quality source images and non-interactive to multiple subjective and objective needs. To solve them, we introduce a novel approach that leverages semantic text guidance image fusion model for degradation-aware and interactive image fusion task, termed as Text-IF. It innovatively extends the classical image fusion to the text guided image fusion along with the ability to harmoniously address the degradation and interaction issues during fusion. Through the text semantic encoder and semantic interaction fusion decoder, Text-IF is accessible to the all-in-one infrared and visible image degradation-aware processing and the interactive flexible fusion outcomes. In this way, Text-IF achieves not only multi-modal image fusion, but also multi-modal information fusion. Extensive experiments prove that our proposed text guided image fusion strategy has obvious advantages over SOTA methods in the image fusion performance and degradation treatment. The code is available at https://github.com/XunpengYi/Text-IF.

We propose a controllable visual enhancer, named DDBF, which is based on cross-modal conditional adversarial learning and aims to dispel darkness and achieve better visible and infrared modalities fusion. Specifically, a guided restoration module (GRM) is firstly designed to enhance weakened information in the low-light visible modality. The GRM utilizes the light-invariant high-contrast characteristics of the infrared modality as the central target distribution, and constructs a multilevel conditional adversarial sample set to enable continuous controlled brightness enhancement of visible images. Then, we develop an information fusion module (IFM) to integrate the advantageous features of the enhanced visible image and the infrared image. Thanks to customized explicit information preservation and hue fidelity constraints, the IFM produces visually pleasing results with rich textures, significant contrast, and vivid colors. The brightened visible image and the final fused image compose the dual output of our DDBF to meet the diverse visual preferences of users. We evaluate DDBF on the public datasets, achieving state-of-the-art performances of low-light enhancement and information integration that is available for both day and night scenarios. The experiments also demonstrate that our DDBF is effective in improving decision accuracy for object detection and semantic segmentation. Moreover, we offer a user-friendly interface for the convenient application of our model. The code is publicly available at https://github.com/HaoZhang1018/DDBF.

The fusion of visible and infrared images aims to produce high-quality fusion images with rich textures and salient target information. Existing methods lack interactivity and flexibility in the execution of fusion. It is unfeasible to express the requirements to modify the fusion effect, and the different regions in the source images are treated equally across the identical fusion model, which causes fusion homogenization and low distinction. Besides, their pre-defined fusion strategies invariably lead to monotonous effects, which are insufficiently comprehensive. They fail to adequately consider data credibility, scene illumination, and noise degradation inherent in the source information. To address these issues, we propose the Te xt-driven and Region-aware Flexible visible and infrared image fusion, termed as TeRF. On the one hand, we propose a flexible image fusion framework with multiple large language and vision models, which facilitates the visual-text interaction. On the other hand, we aggregate comprehensive fine-tuning paradigms for the different fusion requirements to build a unified fine-tuning pipeline. It allows the linguistic selection of the regions and effects, yielding visually appealing fusion outcomes. Extensive experiments demonstrate the competitiveness of our method both qualitatively and quantitatively compared to existing state-of-the-art methods. Our code is publicly available at https://github.com/Baixuzx7/TeRF.

Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, etc. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. To address these challenges, this study proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. First, this framework integrates feature-level information integration into the diffusion process, allowing adaptive degradation removal and multi-modal information fusion. This is the first attempt to deeply and explicitly embed information fusion within the diffusion process, effectively addressing compound degradation in image fusion. Second, by embedding the combination of the text and zero-shot location model into the diffusion fusion process, a text-controlled fusion re-modulation strategy is developed. This enables user-customized text control to improve fusion performance and highlight foreground objects in the fused images. Extensive experiments on diverse public datasets show that our Text-DiFuse achieves state-of-the-art fusion performance across various scenarios with complex degradation. Moreover, the semantic segmentation experiment validates the significant enhancement in semantic performance achieved by our text-controlled fusion re-modulation strategy. The code is publicly available at https://github.com/Leiii-Cao/Text-DiFuse.

In this paper, we present a novel high dynamic range (HDR)-like image generator that utilizes mutual-guided learning between multi-exposure registration and fusion, leading to promising dynamic multi-exposure image fusion. The method consists of three main components: the registration network, the fusion network, and the dual attention network which seamlessly integrates registration and fusion processes. Initially, within the registration network, the estimation of deformation fields among multi-exposure image sequences is conducted following an exposure-invariant feature extraction phase. This leads to enhanced accuracy by mitigating discrepancies across domains. Subsequently, the fusion network utilizes a progressive frequency fusion module in two distinct stages, addressing color correction and detail preservation within low and high-frequency domains, respectively. To facilitate the mutual enhancement of the registration and fusion networks, we undertake a mutual-guided learning strategy encompassing their physical connection and constraint paradigm. Firstly, a dual attention network bridges the registration and fusion networks, addressing ghosting, which is beyond the scope of registration and facilitates information exchange between input images. Secondly, a meticulously designed generative adversarial network-like iterative training schema guides the overall network framework, thereby yielding high-quality HDR-like images through mutual enhancement. Comprehensive experiments on publicly available datasets validate the superiority of our method over existing state-of-the-art approaches.

In this paper, we propose a novel optical flow guided network with progressive frequencies learning, achieving promising dynamic multi-exposure image fusion. Specifically, the proposed method consists of the optical flow alignment block and the progressive frequencies fusion block, where the former is to alleviate the ghost caused by the camera and object motions, and the latter is dedicated to synthesizing the desired color and details. First, in the optical flow alignment block, we estimate the optical flow between two source images and utilize the deformable convolutional network to achieve spatial alignment guided by the estimated optical flow. Second, in the progressive frequencies fusion block, color correction and details preservation are implemented in two gradual phases,i.e., low and high frequencies. For the low frequency fusion phase, we combine the convolutional neural network and swin transformer to capture local and global features, so as to consider the color correction from a complete perspective. For the high frequency fusion phase, an attention gate is designed to evaluate the important details from source images, bringing fewer artifacts and halos. Finally, the low and high frequency fusion phases are connected through a residual mapping strategy to generate a desired image with reasonable colors and rich details. Extensive experiments on publicly available datasets reveal that our method outperforms the state-of-the-art for both static and dynamic scenarios. Moreover, our method is superior in running efficiency over most of the state-of-the-art methods.

Low-light image enhancement (LIME) aims to convert images with unsatisfied lighting into desired ones. Different from existing methods that manipulate illumination in uncontrollable manners, we propose a flexible framework to take user-specified guide images as references to improve the practicability. To achieve the goal, this article models an image as the combination of two components, that is, content and exposure attribute, from an information decoupling perspective. Specifically, we first adopt a content encoder and an attribute encoder to disentangle the two components. Then, we combine the scene content information of the low-light image with the exposure attribute of the guide image to reconstruct the enhanced image through a generator. Extensive experiments on public datasets demonstrate the superiority of our approach over state-of-the-art alternatives. Particularly, the proposed method allows users to enhance images according to their preferences, by providing specific guide images. Our source code and the pretrained model are available at https://github.com/Linfeng-Tang/DRLIE.

In this paper, we rethink the low-light image enhancement task and propose a physically explainable and generative diffusion model for low-light image enhancement, termed as Diff-Retinex. We aim to integrate the advantages of the physical model and the generative network. Furthermore, we hope to supplement and even deduce the information missing in the low-light image through the generative network. Therefore, Diff-Retinex formulates the lowlight image enhancement problem into Retinex decomposition and conditional image generation. In the Retinex decomposition, we integrate the superiority of attention in Transformer and meticulously design a Retinex Transformer decomposition network (TDN) to decompose the image into illumination and reflectance maps. Then, we design multi-path generative diffusion networks to reconstruct the normal-light Retinex probability distribution and solve the various degradations in these components respectively, including dark illumination, noise, color deviation, loss of scene contents, etc. Owing to generative diffusion model, Diff-Retinex puts the restoration of low-light subtle detail into practice. Extensive experiments conducted on real-world low-light datasets qualitatively and quantitatively demonstrate the effectiveness, superiority, and generalization of the proposed method.

In this paper, we propose a novel multi-patch and multi-stage pansharpening method with knowledge distillation, termed as PSDNet. Different from existing pansharpening methods that typically input single-size patches to the network and implement pansharpening in an overall stage, we design multi-patch inputs and a multi-stage network for more accurate and finer learning. First, multi-patch inputs allow the network to learn more accurate spatial and spectral information by reducing the number of object types. We employ small patches in the early part to learn accurate local information, as small patches contain fewer object types. Then, the later part exploits large patches to fine-tune it for the overall information. Second, the multi-stage network is designed to reduce the difficulty of the previous single-step pansharpening and progressively generate elaborate results. In addition, instead of the traditional perceptual loss, which hardly relates to the specific task or the designed network, we introduce distillation loss to reinforce the guidance of the ground truth. Extensive experiments are conducted to demonstrate the superior performance of our proposed PSDNet to existing state-of-the-art methods. Our code is available at https://github.com/Meiqi-Gong/PSDNet.

In this article, we propose a surface- and deep-level constraint-based pan-sharpening network, termed SDPNet, to address the pan-sharpening problem. Focusing on the two primary goals of pan-sharpening, i.e., spatial and spectral information preservations, we first design two encoder-decoder networks to extract deep-level features from two types of source images, in addition to surface-level characteristics, as the enhanced information representation. The unique feature maps that characterize the unique information in source images can be obtained through the deep-level feature extraction. We further design a pan-sharpening network with densely connected blocks to strengthen feature propagation and reduce parameter number, where the unique feature maps are utilized to efficiently constrain the similarity between the pan-sharpened result and the ground truth, thus avoiding information distortion. Both qualitative and quantitative comparisons on the reduced-resolution and full-resolution source images demonstrate the advantages of our method over state-of-the-art methods. Our code is publicly available at https://github.com/hanna-xu/SDPNet.

In this paper, we propose a fast unified image fusion network based on proportional maintenance of gradient and intensity (PMGI), which can end-to-end realize a variety of image fusion tasks, including infrared and visible image fusion, multi-exposure image fusion, medical image fusion, multi-focus image fusion and pan-sharpening. We unify the image fusion problem into the texture and intensity proportional maintenance problem of the source images. On the one hand, the network is divided into gradient path and intensity path for information extraction. We perform feature reuse in the same path to avoid loss of information due to convolution. At the same time, we introduce the pathwise transfer block to exchange information between different paths, which can not only pre-fuse the gradient information and intensity information, but also enhance the information to be processed later. On the other hand, we define a uniform form of loss function based on these two kinds of information, which can adapt to different fusion tasks. Experiments on publicly available datasets demonstrate the superiority of our PMGI over the state-of-the-art in terms of both visual effect and quantitative metric in a variety of fusion tasks. In addition, our method is faster compared with the state-of-the-art.