# WIG-Net: Wavelet-Based Defocus Deblurring with IFA and GCN
## 2 Related Works  
The process of image deblurring can be regarded as the pursuit of an optimal solution in the solution space. Traditional methods employ various natural image priors to constrain the solution space by estimating the maximum a posteriori mode [6]. However,  
Figure 1: Ringing artifacts and remaining blur in the defocus of the deblurring image.  
the conventional optimization approaches involve intricate iterative computations and lack real-time capabilities. Moreover, simplistic model assumptions can lead to inaccurate blur kernel estimation, thereby reducing the algorithm's accuracy. In recent years, with the advancements of deep learning, Convolutional Neural Networks (CNNs) have been extensively applied in the field of image deblurring [7; 8; 9].  
Recent research has primarily focused on enhancing network architectures, introducing multi-scale and increasing receptive fields to enhance the performance of image restoration algorithms. [PERSON] et al. [2] proposed an encoder-decoder structure network (U-net), which effectively exploits contextual information to achieve superior performance in image semantic segmentation. [PERSON] et al. [9] applied multiple scales to defuzzing networks, gradually removing differences in the degree of ambiguity. [PERSON] et al. [10] embedded smooth dilated convolution to the network, while keeping the number of parameters in receptive fields constant to improve network performance. [PERSON] et al. [11] introduced an enhanced U-Net, incorporating depth-wise separable convolutions, residual depth-wise separable convolutions, and wavelet transform. This approach enables the extraction of finer image details while simultaneously reducing computational complexity. [PERSON] et al. [12] introduced a network designed to establish direct mapping from a blurred input image to a clear image, leveraging the estimated defocus map to condition this mapping process. [PERSON] et al. [13] introduced the blind text image deblurring method to obtain a clean text image from the given blurry text image without knowing the blur kernel. However, their algorithm still lags behind mainstream deblurring algorithms in terms of processing time. Additionally, it does not achieve satisfactory results in the task of image deblurring caused by defocusing. [PERSON] et al. [14] cascaded wavelet transform convolution and nonlinear modulus calculations to compute translation-invariant image features, preserving high-frequency information for classification. In another interesting work, [PERSON] et al. [15] discovered that wavelet transform within CNNs is beneficial for single-image super-resolution and introduced wavelet residual networks. [PERSON] et al. [16] presented a deblurring method that employs a two-level wavelet-based convolutional neural network (CNN). This network incorporates discrete wavelet transform (DWT) to distinguish image context from texture information, consequently reducing computational complexity. Additionally, [PERSON] et al. [4] introduced an Iterative Adaptive Network to address spatially varying blur. Their approach enhances the network's deblurring capabilities by predicting a unique deblurring filter for every pixel within the image.  
Nevertheless, current defocus deblurring methods still exhibit certain drawbacks, such as image ringing artifacts and remaining blur. Furthermore, most networks are constrained when dealing with pronounced severe defocus blur, and are characterized by a large number of model parameters, long training times, and significant practical limitations.