Pixel-level fusion of visible and infrared images has demonstrated promise in enhancing information representation. However, nighttime image fusion remains challenging due to low and uneven lighting. Existing fusion methods neglect the preservation of color-related information at night, resulting in unsatisfactory outcomes with insufficient brightness. This paper presents a novel color image fusion framework to prevent color distortion, thus generating results more aligned with human perception. Firstly, we design an image fusion network to retain color information from visible images under low-light conditions. Secondly, we incorporate mature low-light enhancement technology into the network as a flexible component to produce fusion results under normal illumination. The training process is carefully designed to address potential issues of overexposure or noise amplification. Finally, we utilize knowledge distillation to create a lightweight end-to-end network that directly generates fusion results under normal lighting conditions from pairs of low-light images. Experimental results demonstrate that our proposed framework outperforms existing methods in nighttime scenarios.

Hyperspectral image (HSI) denoising algorithms often face challenges with non-uniform, band-dependent noise and fail to fully utilize high signal-to-noise ratio (SNR) spectral bands. To address these issues, this paper presents a zero-shot framework that leverages high-SNR bands as scene-specific references to guide the restoration of degraded bands. The framework features a dual-branch network architecture with the guide branch extracting high-fidelity spatial structures from high-SNR bands and the denoising branch restoring degraded bands through dynamic feature fusion. Fusion modules, enhanced with spectral-spatial attention, enable adaptive and spatially aware refinement between branches. Unlike conventional methods relying on external training datasets or fixed priors, the proposed approach adapts to the scene-specific quality disparities within each HSI. Extensive experiments on synthetic and real-world datasets demonstrate its superior ability in preserving spatial-spectral fidelity and handling complex noise scenarios, outperforming state-of-the-art methods.

Tumor segmentation plays a crucial role in clinical diagnosis and treatment. However, the presence of low contrast, blurred boundaries, and complex morphology in tumor images often leads to inaccuracies in tumor target segmentation. Recent approaches leveraging the Segment Anything Model (SAM) have made significant strides in tumor image segmentation, but they typically rely on precise pixel-level annotations, which are both costly and time-consuming, presenting a major challenge for effective tumor segmentation. To address these issues, we propose MWS-SAM, a novel SAM-based tumor segmentation framework that enables model training using only bounding box annotations, significantly reducing annotation costs while maintaining high segmentation accuracy. Specifically, the Multi-scale Feature Fusion Decoder (MFD) module significantly enhances the model’s capacity to capture tumor edge information by integrating multi-scale features from tumor images. To ease the noise introduced by bounding box annotations, we propose the Prediction-Box-Supervision (PBS) module, which transforms the predicted mask into a box mask aligned with the bounding box annotations, effectively addressing potential misrepresentations due to box bias. Additionally, we implement a consistency loss among varying predictions within the same image, which successfully reduces excessive prediction diversity and enhances overall model robustness. Results obtained from four distinct tumor segmentation datasets validate the efficacy of MWS-SAM, demonstrating comparable performance to fully supervised models without the necessity for accurate annotation.

In real hyperspectral scenes, heterogeneous spatial details and noises make a single subspace assumptions unrealistic. In this letter, a novel transferable multiple tensor subspace learning scheme is proposed for super-resolution enhancement of hyperspectral image (HSI). The intrinsic assumption is that the nonlocal patch tensors extracted from HSIs are derived from multiple tensor low-rank subspaces, which is compatible with practical data distribution and may better characterize the complex structures underlying HSIs. The transferable subspace structures are embedded into both nonblind and semi-blind HSI super-resolution. The alternating direction method of multipliers (ADMMs) algorithm is derived for model learning. The superiority of our method is demonstrated by comprehensive experiments on both synthetic and real datasets.

The fusion of infrared and visible imagery plays a crucial role in environmental monitoring. Existing approaches aim to achieve high-quality perceptual results for human observers and robust outcomes for machine-based high-level tasks by adopting a joint design of fusion and segmentation. However, design constraints imposed by predetermined fusion rules limit the precision of high-level tasks, and drawbacks in the image domains to be fused are often overlooked. This paper presents a novel semantic-powered infrared and visible image fusion framework to address these issues. The key feature of our approach is the utilization of trainable gains and weights in enhancement and fusion processes, influenced solely by segmentation and serving as uncertainty parameters. We propose a two-stage training strategy: initially, training a combined enhancement and fusion network with random uncertainty parameters, followed by the estimation of semantic-driven uncertainty parameters. The enhancement and fusion process is optimized within the Laplacian Pyramid framework to ensure efficient computation. Experimental results highlight the significance of modeling the fusion process with uncertainty for achieving satisfactory fusion and segmentation outcomes.

The fusion of infrared and visible images combines the information from two complementary imaging modalities for various computer vision tasks. Many existing techniques, however, fail to maintain a uniform overall style and keep salient details of individual modalities simultaneously. This paper presents an end-to-end Laplacian Pyramid Fusion Network with hierarchical guidance (HG-LPFN) that takes advantage of pixel-level saliency reservation of Laplacian Pyramid and global optimization capability of deep learning. The proposed scheme generates hierarchical saliency maps through Laplacian Pyramid decomposition and modal difference calculation. In the pyramid fusion mode, all sub-networks are connected in a bottom-up manner. The sub-network for low-frequency fusion focuses on extracting universal features to produce an opposite style while sub-networks for high-frequency fusion determine how much the details of each modality will be retained. Taking the style, details, and background into consideration, we design a set of novel loss functions to supervise both low-frequency images and full-resolution images under the guidance of saliency maps. Experimental results on public datasets demonstrate that the proposed HG-LPFN outperforms the state-of-the-art image fusion techniques.