Image fusion aims to blend complementary information from multiple sensing modalities, yet existing approaches remain limited in robustness, adaptability, and controllability. Most current fusion networks are tailored to specific tasks and lack the ability to flexibly incorporate user intent, especially in complex scenarios involving low-light degradation, color shifts, or exposure imbalance. Moreover, the absence of ground-truth fused images and the small scale of existing datasets make it difficult to train an end-to-end model that simultaneously understands high-level semantics and performs fine-grained multimodal alignment. We therefore present DiTFuse, an instruction-driven Diffusion Transformer (DiT) framework that performs end-to-end, semantics-aware fusion within a single model. By jointly encoding two images and natural-language instructions in a shared latent space, DiTFuse enables hierarchical and fine-grained control over fusion dynamics, overcoming the limitations of pre-fusion and post-fusion pipelines that struggle to inject high-level semantics. The training phase employs a multi-degradation masked-image modeling strategy, so the network jointly learns cross-modal alignment, modality-invariant restoration, and task-aware feature selection without relying on ground truth images. A curated, multi-granularity instruction dataset further equips the model with interactive fusion capabilities. DiTFuse unifies infrared-visible, multi-focus, and multi-exposure fusion-as well as text-controlled refinement and downstream tasks-within a single architecture. Experiments on public IVIF, MFF, and MEF benchmarks confirm superior quantitative and qualitative performance, sharper textures, and better semantic retention. The model also supports multi-level user control and zero-shot generalization to other multi-image fusion scenarios, including instruction-conditioned segmentation.

We propose Pixel2Pixel, a novel zero-shot image denoising framework that leverages the non-local self-similarity of images to generate a large number of training samples using only the input noisy image. This framework employs a compact convolutional neural network architecture to achieve high-quality image denoising. Given a single observed noisy image, we first aim to obtain multiple images with different noise versions. We ensure that the content remains as consistent as possible with the true signal of the noisy image while keeping the noise independent. Specifically, we construct a pixel bank tensor, where each pixel consists of the most similar pixels from the non-local region of the noisy image. Then, multiple training samples, also known as pseudo instances, can be derived from the pixel bank by randomly pixel sampling. By harnessing pixel-wise random sampling, Pixel2Pixel generates a large number of training pseudo instances, thus avoiding reliance on specific training data. In addition, this non-local pixel selection and random sampling strategy helps to break down the spatial correlation of real-world noise as well. Since the proposed method does not require accurate priors on the noise distribution and clean training images, it is suitable for a wide range of noise types and different noise levels, exhibiting strong generalization ability, especially in real noisy scenes. Extensive experiments across various noise types show that Pixel2Pixel outperforms existing methods.

In this paper, we introduce MaeFuse, a novel autoencoder model designed for Infrared and Visible Image Fusion (IVIF). The existing approaches for image fusion often rely on training combined with downstream tasks to obtain high-level visual information, which is effective in emphasizing target objects and delivering impressive results in visual quality and task-specific applications. Instead of being driven by downstream tasks, our model called MaeFuse utilizes a pretrained encoder from Masked Autoencoders (MAE), which facilities the omni features extraction for low-level reconstruction and high-level vision tasks, to obtain perception friendly features with a low cost. In order to eliminate the domain gap of different modal features and the block effect caused by the MAE encoder, we further develop a guided training strategy. This strategy is meticulously crafted to ensure that the fusion layer seamlessly adjusts to the feature space of the encoder, gradually enhancing the fusion performance. The proposed method can facilitate the comprehensive integration of feature vectors from both infrared and visible modalities, thus preserving the rich details inherent in each modal. MaeFuse not only introduces a novel perspective in the realm of fusion techniques but also stands out with impressive performance across various public datasets. The code is available at https://github.com/Henry-Lee-real/MaeFuse.

Infrared images exhibit a significantly different appearance compared to visible counterparts. Existing infrared and visible image fusion (IVF) methods fuse features from both infrared and visible images, producing a new "image" appearance not inherently captured by any existing device. From an appearance perspective, infrared, visible, and fused images belong to different data domains. This difference makes it challenging to apply fused images because their domain-specific appearance may be difficult for downstream systems, e.g., pre-trained segmentation models. Therefore, accurately assessing the quality of the fused image is challenging. To address those problem, we propose a novel IVF method, FusionINV, which produces fused images with an appearance similar to visible images. FusionINV employs the pre-trained Stable Diffusion (SD) model to invert infrared images into the noise feature space. To inject visible-style appearance information into the infrared features, we leverage the inverted features from visible images to guide this inversion process. In this way, we can embed all the information of infrared and visible images in the noise feature space, and then use the prior of the pre-trained SD model to generate visually friendly images that align more closely with the RGB distribution. Specially, to generate the fused image, we design a tailored fusion rule within the denoising process that iteratively fuses visible-style infrared and visible features. In this way, the fused image falls into the visible domain and can be directly applied to existing downstream machine systems. Thanks to advancements in image inversion, FusionINV can directly produce fused images in a training-free manner. Extensive experiments demonstrate that FusionINV achieves outstanding performance in both human visual evaluation and machine perception tasks. The code is available at https://github.com/erfect2020/FusionINV.

Image deblurring continues to achieve impressive performance with the development of generative models. Nonetheless, there still remains a displeasing problem if one wants to improve perceptual quality and quantitative scores of recovered image at the same time. In this study, drawing inspiration from the research of transformer properties, we introduce the pretrained transformers to address this problem. In particular, we leverage deep features extracted from a pretrained vision transformer (ViT) to encourage recovered images to be sharp without sacrificing the performance measured by the quantitative metrics. The pretrained transformer can capture the global topological relations (i.e., self-similarity) of image, and we observe that the captured topological relationships about the sharp image will change when blur occurs. By comparing the transformer features between recovered image and target one, the pretrained transformer provides high-resolution blur-sensitive semantic information, which is critical in measuring the sharpness of the deblurred image. On the basis of the advantages, we present two types of novel perceptual losses to guide image deblurring. One regards the features as vectors and computes the discrepancy between representations extracted from recovered image and target one in Euclidean space. The other type considers the features extracted from an image as a distribution and compares the distribution discrepancy between recovered image and target one. We demonstrate the effectiveness of transformer properties in improving the perceptual quality while not sacrificing the quantitative scores peak signal-to-noise ratio (PSNR) over the most competitive models, such as Uformer, Restormer, and NAFNet, on defocus deblurring and motion deblurring tasks. The code is available at https://github. com/erfect2020/TransformerPerceptualLoss.

In this paper, we propose a new end-to-end model, called dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions. Unlike the pixel-level methods and existing deep learning-based methods, the fusion task is accomplished through the adversarial process between a generator and two discriminators, in addition to the specially designed content loss. The generator is trained to generate real-like fused images to fool discriminators. The two discriminators are trained to calculate the JS divergence between the probability distribution of downsampled fused images and infrared images, and the JS divergence between the probability distribution of gradients of fused images and gradients of visible images, respectively. Thus, the fused images can compensate for the features that are not constrained by the single content loss. Consequently, the prominence of thermal targets in the infrared image and the texture details in the visible image can be preserved or even enhanced in the fused image simultaneously. Moreover, by constraining and distinguishing between the downsampled fused image and the low-resolution infrared image, DDcGAN can be preferably applied to the fusion of different resolution images. Qualitative and quantitative experiments on publicly available datasets demonstrate the superiority of our method over the state-of-the-art.