Snapshot hyperspectral imaging based on narrowband mosaic array encoding suffers from limitations such as low signal-to-noise ratio, limited spectral range, and low spatial resolution. To address these challenges, we propose a novel hyperspectral imaging system that integrates broadband mosaic image with high-resolution (HR) panchromatic (PAN) image of the same scene, establishing a new paradigm for HR hyperspectral image (HSI) acquisition. To fully leverage the complementary information from multi-source images, we introduce a Deep Error-aware Iterative Optimization Network (EIONet), which iteratively reduces reconstruction errors to successfully reconstruct images with both high spatial resolution and high spectral quality. Specifically, we design a Hierarchical Error-aware Cube Updating Mechanism (HECUM) that dynamically partitions image regions based on their reconstruction difficulty during iterations. By prioritizing the enhancement of feature representation in high-difficulty areas, it effectively suppresses the accumulation and propagation of errors. Meanwhile, we employ a Physics-Based Spectral Degradation Modeling approach, constructing a spectral response function with well-defined physical meaning to accurately model the degradation process from the target domain to the observation domain. Experimental results on two public datasets demonstrate that EIONet achieves state-of-the-art performance across multiple evaluation metrics. The related code is available at: https://github.com/Xiexieiii/EIONet.

The fusion of low-resolution hyperspectral images (LR HSI) and high-resolution multispectral images (HR MSI) is a crucial approach for generating hyperspectral images (HSI). However, existing hyperspectral image fusion methods often rely on a single feature mapping process, which makes it difficult to accommodate the significant differences in features between the source images and the real images. Consequently, the generated images frequently exhibit varying degrees of information loss across different spectral bands and limit the overall performance of the fusion. To address this issue, we propose a novel hyperspectral image fusion network. Specifically, an asymptotic spectral mapping module is designed to enhance the detail information fitting capabilities of the fusion network. This module transforms the fitting process of missing information into multiple sets of fitting processes with varying degrees, which can map features with different spectral fidelity to various scales and gradually fit spectral information, thereby reducing spectral distortion. Additionally, we introduce an adaptive defect optimization loss that guides the network to focus on reconstructing regions with substantial spectral differences between LR HSI and HSI, optimizing the network’s constraints regarding the similarity between predicted and real images. Experimental results demonstrate that the proposed fusion network outperforms existing state-of-the-art methods across diverse datasets.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance. To this end, a novel information measurement method is proposed to achieve IR and VI fusion. Its core idea is to guide a generator in achieving image fusion by learning the denoisers. Specifically, by using denoisers to restore fusion images with different noise interference to source images, a mutual competition relationship is formed between denoisers, which helps the generator thoroughly explore the data specificity of the source images and guide it to achieve more accurate feature representation. In addition, a semantic adaptive measurement loss function is proposed to constrain the generator, which fuses semantic information adaptively by considering the semantic information density of different source images. The results of quantitative and qualitative experiments have shown that the proposed method can achieve a higher quality information fusion and has a faster fusion speed on three public datasets when compared with advanced methods.