Image Deblurring Algorithm Incorporating Self-Attention Mechanism

The acquisition of clear images is a critical aspect in various fields including computer vision, aerial detection, and medical imaging. The issue of image blur caused by object motion poses a challenge in obtaining clear images. To address this, an improved AT-DGAN network model is proposed in this paper. This model integrates the pyramid generator module of the DeblurGAN-v2 network with a self-attention mechanism. The feature pyramid is employed for image feature extraction and representation, while the self-attention mechanism dynamically adjusts the weight of important features in each pyramid layer and performs weighted fusion, thereby compensating for the information loss during feature extraction in the feature pyramid network. Additionally, a hinge loss function is designed for the proposed model to balance the discriminator and the generator, enhancing the stability and training efficiency of the generative adversarial network. The experimental results show that compared to other algorithms of the same type, this improved algorithm has increased the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of restored images by 0.58dB and 1.5%, respectively.