# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Based Methods
#### 4.1.3 Multi-Stream Structure  
Because the one-encoder-one-decoder structure can only address a single problem, several matting methods have introduced a multi-stream structure to tackle multi-task problems. By utilizing these approaches, the network is able to acquire and extract a wider range of features from the input image, leading to enhanced flexibility and performance for various tasks.  
According to [PERSON] et al. [13], inferring the alpha matte directly from the trimap can lead to a decrease in algorithm performance due to difficulties in identifying the real mixed pixels in the unknown regions and estimating their alpha values simultaneously. In order to tackle this problem, the authors put forward a new method that decomposes the matting task into two subtasks and developed a one-encoder-two-decoder model to solve both problems simultaneously. This approach enables better utilization of semantic information and provides the model with additional structure awareness and a fault-tolerant capability.  
[PERSON] et al. [14] proposed a context-aware method for image matting in natural settings, which allows for the simultaneous estimation of the foreground and alpha matte. This method investigates the impact of both local regions and global contextual information on high-quality matting and designs a network that contains two encoders and two decoders and integrates global sampling and local propagation strategies to estimate the context-aware foreground and alpha matte in parallel. Through the fusion of regional visual features and global contextual information, the neural network provides sufficient feature information for estimating the alpha matte, effectively improving its accuracy.  
Most matting methods have shown excellent performance on test datasets, but their practical performance can be less impressive due to the high resolution of images and due to hardware limitations. Therefore, [PERSON] et al. [26] proposed a method specifically designed for high-resolution images, which operates by cropping and stitching image patches while explicitly modeling contextual information across them, addressing the issues of context dependency and consistency between different image patches. Specifically, the proposed method employs a dual encoder structure to extract features from both the image block and trimap block. The resulting feature blocks are then processed using a custom cross-patch context module, which captures long-term contextual dependency information across image blocks, making stitching boundaries smoother after restoring all image blocks. This approach was the first to address the challenges of high-resolution image matting.  
[PERSON] et al. [27] discovered that the effective receptive field of ordinary convolutional neural networks (CNNs) is often too small to connect pixels in unknown areas with pixels in known areas, which can significantly affect the performance of the model. To address this issue, the authors designed a two-stream structure model based on the crop and patch approach; one branch called the matching module was used to process cropped image blocks, and the other branch called the propagating module was utilized to process context image blocks centered around the cropped image blocks. This processing strategy allows for the learning of long-range features outside the receptive field, which helps in distinguishing the local foreground and background and in solving the issues mentioned above, ultimately improving the overall performance of the model.