The absence of ground truth (GT) in most fusion tasks poses significant challenges for model optimization, evaluation, and generalization. Existing fusion methods achieving complementary context aggregation predominantly rely on hand-crafted fusion rules and sophisticated loss functions, which introduce subjectivity and often fail to adapt to complex real-world scenarios. To address this challenge, we propose Mask-DiFuser, a novel fusion paradigm that ingeniously transforms the unsupervised image fusion task into a dual masked image reconstruction task by incorporating masked image modeling with a diffusion model, overcoming various issues arising from the absence of GT. In particular, we devise a dual masking scheme to simulate complementary information and employ a diffusion model to restore source images from two masked inputs, thereby aggregating complementary contexts. A content encoder with an attention parallel feature mixer is deployed to extract and integrate complementary features, offering local content guidance. Moreover, a semantic encoder is developed to supply global context which is integrated into the diffusion model via a cross-attention mechanism. During inference, Mask-DiFuser begins with a Gaussian distribution and iteratively denoises it conditioned on multi-source images to directly generate fused images. The masked diffusion model, learning priors from high-quality natural images, ensures that fusion results align more closely with human visual perception. Extensive experiments on several fusion tasks, including infrared-visible, medical, multi-exposure, and multi-focus image fusion, demonstrate that Mask-DiFuser significantly outshines SOTA fusion alternatives.

Existing infrared-visible image fusion methods are mainly tailored for high-quality source images. Although recent studies have begun to explore degradation-aware fusion, most existing methods still focus on specific degradation types, while unified frameworks that aim to handle diverse degradations often depend on auxiliary textual prompts, which limits their practicality in automatic fusion scenarios. This work presents a Degradation and Semantic Prior dual-guided framework for degraded image Fusion (DSPFusion), which jointly performs degradation-aware restoration and complementary information aggregation in a unified architecture without relying on auxiliary prompts. Specifically, it first extracts modality-specific degradation priors from degraded infrared and visible images, while capturing compact semantic embeddings from paired source images as low-quality semantic priors to encode global scene context. Then, a semantic prior diffusion model is devised to restore high-quality scene semantic priors in a compact latent space, providing global scene guidance with low computational overhead and enabling over $30\times $ inference speedup compared with mainstream diffusion model-based image fusion schemes, such as DDFM. Guided by the restored semantic priors and degradation priors, the enhancement and fusion network adaptively suppresses degradations and aggregates complementary information. Extensive experiments under both degraded and normal scenarios demonstrate that DSPFusion effectively handles representative degradations, preserves complementary information, and achieves competitive performance with low computational cost, thereby broadening the practical application scope of image fusion. The source code is publicly available at https://github.com/Linfeng-Tang/DSPFusion.

The absence of real-world ground truth (GT) remains a challenge in multi-exposure image fusion (MEF). Benchmarks synthesizing pseudo GT through algorithm ensembles. Existing methods, hampered by inherent imperfections of pseudo GT and fixed mapping relationships, show limited performance and robustness. To address the limitations, we propose a novel cross-modal diffusion framework that synergizes text prompts and semantic perception for MEF, termed as Diff-MEF. First, it reformulates MEF as a probabilistic estimation task with conditional diffusion model for progressive transition and fusion. Then, we explicitly infer semantic and exposure priors as text prompts and semantic perception to improve performance and robustness. The priors are synergized through multi-modal prior embedding and optimization guidance. On the one hand, regarding cross-modal interaction, multi-modal priors, including segmentation masks, and exposure- and content-aware text prompts, are embedded into diffusion process by dedicated encoders and refine visual features through a text-segmentation refinement module. On the other hand, a semantic-level contrastive loss builds a regularization between cross-modal features in the semantic space of CLIP to mitigate degradations introduced by pseudo GT and fusion distortions. Experiments demonstrate that Diff-MEF outperforms SOTA methods and pseudo GT with superior fusion performance and robustness across diverse exposure scenarios. Code is available at https://github.com/hanna-xu/Diff-MEF.

This prospective study proposes CoMatch, a novel semi-dense image matcher with dynamic covisibility awareness and bilateral subpixel accuracy. Firstly, observing that modeling context interaction over the entire coarse feature map elicits highly redundant computation due to the neighboring representation similarity of tokens, a covisibility-guided token condenser is introduced to adaptively aggregate tokens in light of their covisibility scores that are dynamically estimated, thereby ensuring computational efficiency while improving the representational capacity of aggregated tokens simultaneously. Secondly, considering that feature interaction with massive non-covisible areas is distracting, which may degrade feature distinctiveness, a covisibility-assisted attention mechanism is deployed to selectively suppress irrelevant message broadcast from non-covisible reduced tokens, resulting in robust and compact attention to relevant rather than all ones. Thirdly, we find that at the fine-level stage, current methods adjust only the target view's keypoints to subpixel level, while those in the source view remain restricted at the coarse level and thus not informative enough, detrimental to keypoint location-sensitive usages. A simple yet potent fine correlation module is developed to refine the matching candidates in both source and target views to subpixel level, attaining attractive performance improvement. Thorough experimentation across an array of public benchmarks affirms CoMatch's promising accuracy, efficiency, and generalizability.

Current image fusion methods struggle with real-world composite degradations and lack the flexibility to accommodate user-specific needs. To address this, we propose ControlFusion, a controllable fusion network guided by language-vision prompts that adaptively mitigates composite degradations. On the one hand, we construct a degraded imaging model based on physical mechanisms, such as the Retinex theory and atmospheric scattering principle, to simulate composite degradations and provide a data foundation for addressing realistic degradations. On the other hand, we devise a prompt-modulated restoration and fusion network that dynamically enhances features according to degradation prompts, enabling adaptability to varying degradation levels. To support user-specific preferences in visual quality, a text encoder is incorporated to embed user-defined degradation types and levels as degradation prompts. Moreover, a spatial-frequency collaborative visual adapter is designed to autonomously perceive degradations from source images, thereby reducing complete reliance on user instructions. Extensive experiments demonstrate that ControlFusion outperforms SOTA fusion methods in fusion quality and degradation handling, particularly under real-world and compound degradations.

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.

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.

Existing multi-modal image fusion algorithms are typically designed for high-quality images and fail to tackle degradation (e.g., low light, low resolution, and noise), which restricts image fusion from unleashing the potential in practice. In this work, we present Degradation-Robust Multi-modality image Fusion (DRMF), leveraging the powerful generative properties of diffusion models to counteract various degradations during image fusion. Our critical insight is that generative diffusion models driven by different modalities and degradation are inherently complementary during the denoising process. Specifically, we pre-train multiple degradation-robust conditional diffusion models for different modalities to handle degradations. Subsequently, the diffusion priori combination module is devised to integrate generative priors from pre-trained uni-modal models, enabling effective multi-modal image fusion. Extensive experiments demonstrate that DRMF excels in infrared-visible and medical image fusion, even under complex degradations. Our code is available at https://github.com/Linfeng-Tang/DRMF.

Image fusion aims to integrate the complementary information of source images and synthesize a single fused image. Existing image fusion algorithms apply hand-crafted fusion rules to merge deep features which cause information loss and limit the fusion performance of methods since the uninterpretability of deep learning. To overcome the above shortcomings, we propose a learnable fusion rule for infrared and visible image fusion based on class activation mapping. Our proposed fusion rule can selectively preserve meaningful information and reduce distortion. More specifically, we first train an encoder-decoder network and an auxiliary classifier based on the shared encoder. Then, the class activation weights are taken out from the auxiliary classifier, which indicates the importance of each channel. Finally, the deep features extracted by the encoder are adaptively fused according to the class activation weights and the fused image is reconstructed from the fused features via the pre-trained decoder. Note that our learnable fusion rule can automatically measure the importance of each deep feature without human participation. Moreover, it fully preserves the significant features of source images such as salient targets and texture details. Extensive experiments manifest our superiority over state-of-the-art algorithms. Visualization of feature maps and their corresponding weights reveals the high interpretability of our method.

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.