In the context of laser interference in electro-optical imaging reconnaissance equipment, interference spots often appear in the imagery. These laser jamming spots significantly degrade image quality and obscure target information, severely impacting detection and tracking systems′ performance. For addressing laser jamming images in typical target scenarios, an inpainting network is developed based on global semantic learning and salient object awareness. A gated semantic learning mechanism is specifically proposed. Initially, a contextual attention mechanism is employed to establish long-range correlations between the interfered and known regions, enabling the inference of content in the interfered regions. Then, a multi-scale feature aggregation module refines the inferred content across different receptive fields, reconstructing rich semantic information in the interfered areas. Finally, a gating mechanism adaptively fuses features from the known and reconstructed regions, enhancing the global semantic consistency of the restored image. Additionally, a salient target consistency loss is designed to guide the inpainting network in perceiving salient targets, improving the sharpness of object contours and texture coherence using a gradient penalty method based on the salient target mask. Experimental results in typical target scenarios such as aircraft, bridges, and roads demonstrate that the proposed network outperforms other methods in generating visually realistic and complete content, with good generalization performance in dealing with complex interference spots.