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Infrared technology plays a crucial role in various fields. However, defocus blur occurs in infrared images due to improper focusing, resulting in undesirable blurring effects. In recent years, deep learning-based methods have achieved remarkable success in image restoration. However, the spatial variability of defocus blur, combined with the low resolution and lack of textural details in infrared images, still presents significant challenges for deblurring. In this paper, we propose a CNN-based neural network that employs dynamic large separate kernel convolutions to adapt to real-world defocus blur patterns and to effectively extract blur features. Furthermore, we introduce an encoder-decoder feature fusion module that incorporates edge attention, spatial attention, and channel attention to enhance the network’s focus on edges while selectively processing relevant information, thereby improving the network’s deblurring performance. Experimental results demonstrate that our method outperforms recent advanced methods in handling defocus blur in infrared images. Additionally, ablation studies are provided to show the effectiveness of the proposed modules.
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