Existing infrared and visible image fusion methods often face the dilemma of balancing modal information. Generative fusion methods reconstruct fused images by learning from data distributions, but their generative capabilities remain limited. Moreover, the lack of interpretability in modal information selection further affects the reliability and consistency of fusion results in complex scenarios. This manuscript revisits the essence of generative image fusion under the inspiration of human cognitive laws and proposes a novel infrared and visible image fusion method, termed HCLFuse. First, HCLFuse investigates the quantification theory of information mapping in unsupervised fusion networks, which leads to the design of a multi-scale mask-regulated variational bottleneck encoder. This encoder applies posterior probability modeling and information decomposition to extract accurate and concise low-level modal information, thereby supporting the generation of high-fidelity structural details. Furthermore, the probabilistic generative capability of the diffusion model is integrated with physical laws, forming a time-varying physical guidance mechanism that adaptively regulates the generation process at different stages, thereby enhancing the ability of the model to perceive the intrinsic structure of data and reducing dependence on data quality. Experimental results show that the proposed method achieves state-of-the-art fusion performance in qualitative and quantitative evaluations across multiple datasets and significantly improves semantic segmentation metrics. This fully demonstrates the advantages of this generative image fusion method, drawing inspiration from human cognition, in enhancing structural consistency and detail quality.

Harmful algal bloom (HAB) has long been one of the most formidable environmental problems in the world. HAB is influenced by multifactors, and its dynamic is highly nonstationary, making its prediction challenging. The existing machine learning (ML)-based HAB prediction methods mainly use time-series data, which ignore the intrinsic relationship between spatial and temporal variations in HAB. To achieve more accurate HAB spatiotemporal prediction, a novel long short-term memory (LSTM)-based nonstationary focusing prediction model (HabLSTM) is proposed in this article. The HabLSTM network is constructed by stacking HabLSTM units consisting of the hidden states spatial differential block (HSSD) and the combined states temporal differential (CSTD) block. The HSSD block uses the gating mechanism and the difference in hidden states to generate differential features between adjacent frames and guides the network to learn short-term nonstationary features by controlling the feature update of the hidden state in the HabLSTM unit. The CSTD block uses the gating mechanism and the difference in combined states to generate the differential features of the current input sequence and guides the network to learn long-term nonstationary features by controlling the feature update of the memory state in the HabLSTM unit. These two differential features guide the HabLSTM network to focus on learning nonstationary spatiotemporal features and boost HAB spatiotemporal prediction accuracy. In addition, two new spatiotemporal datasets of HAB named as Taihu HAB A and Taihu HAB B are established using the year-A and year-B normalized difference vegetation index (NDVI) images collected by Himawari-8 satellite, respectively. The experimental results on the two HAB datasets and spatiotemporal predictive learning (ST-PL) benchmark dataset MovingMNIST++ validate the outstanding HAB prediction and nonstationary spatiotemporal features’ learning capability of HabLSTM. The source code is available athttps://github.com/lxq-jnu/HabLSTM.

This paper proposes an image fusion framework based on separate representation learning, called IFSepR. We believe that both the co-modal image and the multi-modal image have common and private features based on prior knowledge, exploiting this disentangled representation can help to image fusion, especially to fusion rule design. Inspired by the autoencoder network and contrastive learning, a multi-branch encoder with contrastive constraints is built to learn the common and private features of paired images. In the fusion stage, based on the disentangled features, a general fusion rule is designed to integrate the private features, then combining the fused private features and the common feature are fed into the decoder, reconstructing the fused image. We perform a series of evaluations on three typical image fusion tasks, including multi-focus image fusion, infrared and visible image fusion, medical image fusion. Quantitative and qualitative comparison with five state-of-art image fusion methods demonstrates the advantages of our proposed model.

To avoid the introduction of false information during the fusion progress, a novel multi-focus image fusion method is proposed in quaternion wavelet transform domain. To obtain the dependency in different high frequency subbands, a quaternion wavelet contextual hidden Markov model (Q-CHMM) is established for modeling quaternion wavelet coefficients. And for better image representations, several features are proposed by analyzing the transform coefficients, phases of coefficients and the statistical attribution of coefficients. Different from the traditional fusion methods basing on a single feature, a comprehensive feature is constructed by using quaternion matrix to fuse the high frequency subbands. Experimental results demonstrate that the proposed method possess good fusion performance.

In this paper, a novel region segmentation and sigmoid function based image fusion method is proposed. Different from the traditional fusion approaches limiting to a single fusion strategy, the proposed method is designed with an adaptive multi-strategy fusion rule (AMFR). In our method, the source images are decomposed into low frequency sub bands and high frequency sub bands via the shift-invariant Shear let transform (SIST). The low frequency sub bands are fused by the choose-max scheme and the high frequency sub bands are fused by the AMFR based on a sigmoid function. The AMFR includes the choose-max scheme and the weighted average scheme, which of them is selected is determined by the sigmoid function. The fused sub bands are merged to reconstruct fused image by using inverse SIST. Experiments conducted on various types of source images demonstrate that our approach achieve superior results compared with the existing fusion methods in both visual presentation and objective evaluation.

In this paper, a new Contextual hidden Markov Model (CHMM) and modified Pulse Coupled Neural Network (M-PCNN) based fusion approach in the Contour domain is proposed for multi-modal medical image fusion. The Contour transform as an emerging multi-scale multi-direction geometric analyzing tool can provide an efficient and flexible representation of images, e.g. edges, contours and textures, which overcomes the drawback of the 2-D wavelet transform. Considering the powerful advantages for statistical modeling and processing of Contour let coefficients by HMM, the context information integrated with HMM is established to construct a comprehensive statistical correlative model, which can collectively capture persistence across scales, directional selectivity within scales and energy concentration in the spatial neighborhood of the high-frequency sub-band coefficients. Low-frequency sub-band coefficients are fused by the magnitude maximum rule, and a modified PCNN is developed where the linking strength of each neuron is determined by the normalized region energy of Edge PDF and modified spatial frequency is employed as the image feature to motivate M-PCNN. The high-frequency directional sub-band coefficients are selected by total pulse number maximum strategy. The experimental results demonstrate that the presented fusion method can further improve fusion image quality and visual effects.