# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 1 Introduction  
Image deblurring aims to restore a high-quality image by removing blurring degradations [1]. Due to the object movements, the limitations of cameras, or intricate light conditions, different degrees of blurring degradations are inevitably introduced during the image-acquisition process. Since these degradations usually lead to unsatisfactory visual perception, they consequently hinder subsequent high-level vision tasks where sharp images can perform better. Moreover, each degraded image may have numerous restoration possibilities as it belongs to an ill-posed problem [2]. Therefore, image de-blurring is a challenging but significant research work. Due to neural networks' advantage of learning features from large-scale datasets, deep-learning-based methods play a dominant role in image-restoration fields. At first, most deep-learning-based deblurring algorithms commonly focused on utilizing the neural network to estimate the blur kernel and then used the blur kernel to restore images [3; 4; 5; 6]. As a result, these kinds of methods heavily depend on the prediction of blur kernels and are not suitable for blurry images caused by various blur kernels. Afterward, with the rapid development of deep learning, it was found that neural networks are capable of much more than blur kernel estimation. Thus, recent deep-learning-based models have started to adopt an end-to-end training strategy, which can directly obtain the complex mappings between sharp and blurry images [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. Among the existing deblurring models, convolution neural networks (CNNs) have been frequently employed to encode semantic information by stacking many convolutionlayers and downsample operations. However, the CNN methods still have limited receptive fields and the downsample operations can hinder detail recovery. In addition, transformers explore the potential of self-attention and perform well in establishing long-range pixel-wise or channel-wise relationships. Unfortunately, transformers' ability to obtain local features is not as strong as CNNs. In general, convolution has powerful local modeling capabilities [20] and usually models the relationships between neighborhood pixels. In contrast, a transformer is a global operation that models the relationships between all pixels [20]. Generally speaking, local features and global features can cooperate to extract more detailed image features. However, how to combine the advantages of CNNs and transformers is worth investigating. Aiming at this goal, this paper proposes a new network. Like most deblurring models, we also utilized the encoder-decoder structure to extract multi-scale features, and then recover images by the coarse-to-fine approach. To fully integrate the multi-scale input images and features from the previous stage, we designed the channel attention fusion block (CAFB) in the encoder path. Specifically, we devised a local-global feature combination block (LGFCB) to extract local and global features. The LGFCB is composed of two parts: three multi-scale residual blocks (MRB) and a transformer block. The MRB is a designed CNN structure, and the transformer block is employed to explore long-range relationships of images. The CNN branch and transformer branch extract features in parallel paths. Then, we introduce cross-path feature fusion so that we can better use the advantages of the CNN and transformer. In addition, we adopt a supervised attention block (SAB) in the decoder path, which utilizes the restoration-image-producing attention map to supervise and obtain more informative features. In this paper, the main contributions are as follows:  
An elaborately designed local-global feature combination block (LGFCB) composed of multi-scale residual blocks (MRB) and transformer blocks. This combination is beneficial for retaining local image details and exploring long-range global features. A channel attention fusion block (CAFB) fuses features and can improve the capability of feature representation.