HCTIRdeblur: A hybrid convolution-transformer network for single infrared image deblurring

Infrared images captured by mobile platforms often suffer image blurs such as defocus blur and motion blur, which seriously degrade the quality of infrared images. However, the existing image deblurring methods generally focused on visible image deblurring while failing to perform infrared image deblurring effectively, due to the infrared images with low resolution, lack of detailed textural information, and tend to fused objects with backgrounds when low-level temperature difference. To this end, this study proposed a novel end to end network for single infrared image blind deblurring. An encoder contains multiple hybrid convolution-transformer feature extraction blocks is designed to effectively extract inherent characteristics of infrared image. The bidirectional feature pyramid structured decoder with full scale connections is adopted to achieve fully reuse multi-stage features and reconstructed clear infrared images ideally. The multi-stage training strategy and a novel mixed loss function are introduced to speed up the convergence of training process and obtain better image deblurring performance. Moreover, a dataset dedicated to infrared images blind deblurring is constructed to facilitate the task of infrared image deblurring. Extensive ablation studies and comparison experiments have been conducted on the test set of the proposed infrared image deblurring dataset. The experimental results demonstrated the effectiveness of the proposed network structure and the superiority of the proposed network over other state of the arts deblurring methods. Finally, comparative experiment is conducted on real captured blurred infrared images and the results verified the superiority and robustness of the proposed network over other existing image deblurring methods.