Multi-Frame Adaptive Image Enhancement Algorithm for vehicle-mounted dynamic scenes

To address the issue of image blurring caused by high-speed vehicle motion and complex road conditions in autonomous driving scenarios, this paper proposes a lightweight Multi-frame Adaptive Image Enhancement Network (MAIE-Net). The network innovatively introduces a hybrid motion compensation mechanism that integrates optical flow alignment and deformable convolution, effectively solving the non-rigid motion alignment problem in complex dynamic scenes. Additionally, a temporal feature enhancement module is constructed, leveraging 3D convolution and attention mechanisms to achieve adaptive fusion of multi-frame information. In terms of architecture design, an edge-guided U-Net structure is employed for multi-scale feature extraction and reconstruction. The framework incorporates edge feature extraction and attention mechanisms within the encoder–decoder to balance feature representation and computational efficiency. The overall lightweight design enables the model to adapt to in-vehicle computing platforms. Experimental results demonstrate that the proposed method significantly improves image quality while maintaining efficient real-time processing capabilities, effectively enhancing the environmental perception performance of in-vehicle vision systems across various driving scenarios, thereby providing a reliable visual enhancement solution for autonomous driving.