Deep learning techniques have revolutionized the infrared and visible image fusion (IVIF), showing remarkable efficacy on complex scenarios. However, current methods do not fully combine frequency domain features with global semantic information, which will result in suboptimal extraction of global features across modalities and insufficient preservation of local texture details. To address these issues, we propose Wavelet-Mamba (W-Mamba), which integrates wavelet transform with the state-space model (SSM). Specifically, we introduce Wavelet-SSM module, which incorporates wavelet-based frequency domain feature extraction and global information extraction through SSM, thereby effectively capturing both global and local features. Additionally, we propose a cross-modal feature attention modulation, which facilitates efficient interaction and fusion between different modalities. The experimental results indicate that our method achieves both visually compelling results and superior performance compared to current state-of-the-art methods. Our code is available at https://github.com/Lmmh058/W-Mamba.

ABSTRACT High‐quality retinal fundus images are widely used by ophthalmologists for the detection and diagnosis of eye diseases, diabetes, and hypertension. However, in retinal fundus imaging, the reduction in image quality, characterized by poor local contrast and non‐uniform brightness, is inevitable. Image enhancement becomes an essential and practical strategy to address these issues. In this paper, we propose a retinal fundus image enhancement method that emphasizes details and equalizes brightness, based on image decomposition. First, the original image is decomposed into three layers using an edge‐preserving filter: a base layer, a detail layer, and a noise layer. Second, an adaptive local power‐law approach is applied to the base layer for brightness equalization, while detail enhancement is achieved for the detail layer through saliency analysis and blue channel removal. Finally, the base and detail layers are combined, excluding the noise layer, to synthesize the final image. The proposed method is evaluated and compared with both classical and recent approaches using two widely adopted datasets. According to the experimental results, both subjective and objective assessments demonstrate that the proposed method effectively enhances retinal fundus images by highlighting details, equalizing brightness, and suppressing noise and artifacts, all without causing color distortion.

Safe urban driving is challenging due to the high density of traffic flow and various potential hazards, such as the sudden appearance of unknown objects. Traditional rule-based approaches and imitation learning methods struggle to address the diverse driving scenarios encountered in urban environments. Reinforcement learning (RL), which adapts to a wide range of driving scenarios through continuous interaction with the environment, has demonstrated success in autonomous driving. Making safety decisions when driving in urban environments necessitates a comprehensive perception of the current scene and the ability to predict the evolution of the dynamic scene. In this paper, we present a novel cascade deep reinforcement learning framework, CDRP3, designed to enhance the safety decision-making capabilities of self-driving vehicles in complex scenarios and emergencies. We leverage a multi-modal spatio-temporal perception (MmSTP) module to fuse multi-modal sensor data and introduce temporal perception to capture spatio-temporal information about dynamic driving environments, and a future state prediction (FSP) module to model complex interactions between different traffic participants and explicitly predict their future states. Subsequently, in the PPO-based planning module, we use the comprehensive environmental information obtained from perception and prediction to decode an optimized driving strategy using a lateral and longitudinal separated multi-branch network structure guided by a customized reward function. This approach enables knowledge transfer from the perception and prediction components to planning, and planning-oriented enhancement of safety decision-making capabilities to improve driving safety. Our experiments demonstrate that CDRP3 outperforms state-of-the-art methods, providing superior driving safety in complex urban environments.

Abstract Infrared image enhancement is an effective way to solve contrast reduction or details degradation in infrared imagery. An infrared enhancement approach based on local saliency extraction is proposed here. First, saliency maps are extracted within a local window by combining spatial weight. Second, with the change of the window size, potential targets and details in different sizes can be extracted. Considering window sizes as the scales, the saliency maps are obtained and infrared images are enhanced at different scales, and finally, multi‐scale fusion is used to achieve the enhancement. Eight popular infrared enhancement approaches are introduced for comparison. Subjective qualitative observation experiments show that our strategy based on local saliency analysis and multi‐scale fusion can well extract potential targets and areas of varying size from the source images with different sizes, to obtain a good enhancement effect. Meanwhile, we introduce six objective evaluation methods to measure the results, and the evaluation data to prove the effectiveness of the proposed algorithm. The experiments also indicate the real‐time processing capability of the proposed method. The proposed method is finally well applied in the hardware system and shows its good performance.