Fully-Cascaded Spatial-Aware Convolutional Network for Motion Deblurring

Motion deblurring is an ill-posed, challenging problem in image restoration due to non-uniform motion blurs. Although recent deep convolutional neural networks have made significant progress, many existing methods adopt multi-scale or multi-patch subnetworks that involve additional inter-subnetwork processing (e.g., feature alignment and fusion) across different scales or patches, leading to substantial computational cost. In this paper, we propose a novel fully-cascaded spatial-aware convolutional network (FSCNet) that effectively restores sharp images from blurry inputs while maintaining a favorable balance between restoration quality and computational efficiency. The proposed architecture consists of simple yet effective subnetworks connected through a fully-cascaded feature fusion (FCFF) module, enabling the exploitation of diverse and complementary features generated at each stage. In addition, we design a lightweight spatial-aware block (SAB), whose core component is a channel-weighted spatial attention (CWSA) module. The SAB is integrated into both the FCFF module and skip connections, enhancing feature fusion by enriching spatial detail representation. On the GoPro dataset, FSCNet achieves 33.01 dB PSNR and 0.962 SSIM, delivering comparable or higher accuracy than state-of-the-art methods such as HINet, while reducing model size by nearly 80%. Furthermore, when the GoPro-trained model is evaluated on three additional benchmark datasets (HIDE, REDS, and RealBlur), FSCNet attains the highest average PSNR (29.53 dB) and SSIM (0.903) among all compared methods. This consistent cross-dataset superiority highlights FSCNet’s strong generalization and robustness under diverse blur conditions, confirming that it achieves state-of-the-art performance with a favorable performance–complexity trade-off.