Real-world image deblurring using data synthesis and feature complementary network

Many learning-based approaches to image deblurring have received increasing attention in recent years. However, the models trained on existing synthetic datasets do not generalize well to real-world blur, resulting in undesirable artifacts and residual blur. This work attempts to address this problem from two aspects: training data synthesis and network architecture. To narrow the domain gap between synthetic and real domains, a realistic blur synthesis pipeline to generate high-quality blurred data is proposed. Since the blur is non-uniform and has different scales and degrees, a parallel feature complementary module to fully exploit the local and non-local information, which improves the feature representation and helps the network to perceive the non-uniform blur, is developed. In addition, a spatial Fourier reconstruction block to facilitate correct detail recovery in the spatial and Fourier domains is introduced. Based on these two designs, an effective encoder-decoder network for deblurring is designed. Extensive experiments demonstrate the validity and superiority of the proposed blur synthesis method and deblurring network. In particular, the proposed deblurring network can achieve superior or comparable performance to Restormer, while saving 70% of network parameters and 53% of floating point operations (FLOPs). A realistic blur synthesis pipeline to generate high-quality blurred data and design an effective encoder-decoder network that benefits from the complementary modelling capabilities of convolutional neural networks and transformers for deblurring is proposed. Extensive experiments demonstrate the validity and superiority of the proposed blur synthesis method and deblurring network.image