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• An efficient non-uniform motion deblurring network is proposed to introduce edge information to guide image deblurring task. • A new Multi-stream Adaptive Fusion Module (MAFM) is designed to focus on crucial information in different information flows. • A novel Dense Attention Feature Extraction Module (DAFEM) is developed to obtain significant recovery information. • Edge loss and perceptual loss are introduced to optimize the generator to improve the image restoration effect. Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as network inputs and Edge Extraction Network (EEN) guides the Deblurring Network (DN) for image recovery and to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in terms of PSNR, SSIM and VIF, and generates images with less blur and sharper edges. [ABSTRACT FROM AUTHOR]

• We propose a cascading Transformer to address the computational bottleneck of the Transformer in handling complex tasks with blurry images. • We propose the Deep Separable Attention (DSA) mechanism to replace the vanilla self-attention mechanism, which can capture specific areas of blur in the image and calculate attention scores across feature dimensions. • We propose the Double-Flow Gate (DFG) to enhance the model's nonlinearity, control the output of specific blurry information, and improve deblurring performance. In dynamic scenes, motion blur can often occur, which is non-uniform and can be difficult to remove. Recently, the Transformer has shown excellent performance in various image-related tasks such as classification, recognition, and segmentation. Using a Transformer-based backbone network has also shown potential advantages in image deblurring. However, the computational complexity of Transformers increases quadratically with spatial resolution, making it difficult to apply to high-resolution images. To address the above issue, we propose a cascade Transformer (Casformer) that consists of two key modules: Deep Separable Attention (DSA) and Double-Flow Gate (DFG). Our approach effectively reduces computational complexity while suppressing blurry information. Additionally, we discovered an inconsistency between training and testing images during the image restoration process. We addressed this issue by experimentally verifying an inference aggregation method (IAM) that independently predicts patches during inference to address the problem of imbalanced information distribution. Experimental results demonstrate that our design performs well on GoPro and other datasets, e.g. 29.20 dB PSNR on RealBlur-J, exceeding the previous state-of-the-art (SOTA) 0.14 dB. [ABSTRACT FROM AUTHOR]