基于双域特征融合的多尺度运动图像去模糊OA

To address the issue of motion blur in images captured from dynamic scenes,which degrades image quality and causes serious loss of detail information,we propose a multi-scale motion image deblurring method based on dual-domain feature fusion.First,a Dual-Domain Feature Fusion Block(DDFFB)is designed,which employs a two-branch structure to extract spatial-and frequency-domain features from blurred images in parallel,followed by deep fusion of dual-domain features to enhance the model's capability for representing high-frequency details.Next,a Multi-Scale Feature Aggregation Module(MSFAM)is introduced,which utilizes cross-channel self-attention to aggregate the encoded features from different scales of blurred images and dynamically adjusts the weights of feature maps at these scales to enhance model robustness.Furthermore,the training loss function is improved,and the train-ing of the network model is supervised using a joint multi-scale loss function combining content loss,wavelet domain reconstruction loss and edge loss,thereby enhancing the deblurring performance.Comparative experiments conduc-ted on two public datasets,GoPro and HIDE,demonstrate that the proposed method achieves Peak Signal-to-Noise Ratio(PSNR)values of 32.56 dB and 30.76 dB,respectively,surpassing all other compared methods.The results indicate that our approach effectively improves blurring performance and exhibits strong robustness.