# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 2 Related Work  
### Defocus Deblurring  
The task of defocus deblurring can be divided into two main categories based on whether the defocus blur kernel is estimated: one is kernel-based estimation methods, and the other is kernel-free methods.  
In kernel-based estimation methods, [PERSON] et al. [14] utilized a skeleton image (an image that preserves strong gradients while smoothing out details) as the foundation for estimating the blur kernel. By alternately solving for the skeleton image and blur kernel, blind image deblurring was achieved. [PERSON] et al. [15] proposed estimating spatially variant defocus blur kernels by calculating the reblurred gradient magnitude in edge features. They extended the estimated result to the entire image using edge-connective filters and fast guided filters. In more recent methods, [PERSON] et al. [7] introduced a deep learning-based defocus deblurring method for single images using inverse convolution kernels. By incorporating kernel-sharing parallel atrous convolution (KPAC) blocks, they leveraged the invariance of inverse convolution kernel shapes to efficiently handle blur kernels at different scales. [PERSON] et al. [8] proposed a pixel-based Gaussian Kernel Mixture (GKM) model, which effectively represents spatially variant defocus blur kernels in a linear parameterized form.  
The precise estimation of the blur kernel directly impacts the effectiveness of defocus deblurring. However, achieving such precise estimation is often quite challenging. Some researchers have further explored methods that do not rely on blur kernel estimation. [PERSON] et al. [16] proposed a simple yet effective blur feature based on sparse representation and image decomposition. Through sparse edge representation, they directly established a correspondence with blur intensity estimation. [PERSON] et al. [4] introduced DMENet, an end-to-end convolutional neural network that directly estimates the defocus degree of each pixel in the image, thereby avoiding the blur kernel estimation step. [PERSON] et al. [9] designed a deep neural network architecture, DPDNet, which uses two sub-aperture view data captured by dual-pixel sensors on modern cameras to reduce defocus blur.  
Figure 1: CMDDNet achieves competitive deblurring performance with low resource consumption.  
In 2021, [PERSON] et al. [17] proposed a synthetic dual-pixel data approach to overcome the challenges of acquiring real dual-pixel data and introduced a recursive convolutional network architecture, MDPNet, which enhanced defocus deblurring performance.  
These methods, by utilizing data-driven strategies or neural network self-learning, perform blur processing directly without manual design or estimation of the blur kernel, simplifying the process and adapting to complex blur patterns and diverse scenarios in real-world applications. This improves both the effectiveness and flexibility of deblurring. In this paper, we adopt a similar approach, leveraging a large dataset to further enhance the performance of the defocus deblurring model. By training the model on various types of blur scenarios, it effectively removes blur under complex conditions, improving its generalization capability and ensuring its broad applicability in real-world applications.