Performance Improvement of Laser Interference Image Restoration Based on Multi-Scale Feature Fusion

To address the limitations of traditional image restoration techniques in accurately restoring laser interference images, this paper proposes a nove deep learning framework. This framework leverages convolutional neural networks and a multi-head attention mechanism to extract multi-scale features, thereby enhancing the understanding and restoration of image structures. Experiments are conducted on a synthetic laser interference image dataset comprising 5 scenes, each scene containing 5000 images. Experimental results reveal that the proposed framework visually restores images affected by laser interference and achieves high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In particular, the PSNR and SSIM values for the reconstructed images, across various levels of image damage, exceed 34 dB and 0. 98, respectively. The proposed method holds promise for broad applications in laser interference scenarios and offers valuable support for military defense and civilian technologies.