# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.4 User-Friendly Input  
The process of creating a trimap requires a significant amount of time and effort. When there is no trimap available, trimap-free methods often extract all salient objects in an image,making it challenging to extract specific foregrounds based on user requirements. Therefore, designing a more user-friendly auxiliary input or interactive segmentation methods would meet user demands and also have high research value.  
[PERSON] et al. [21] proposed a portrait matting method using a background image as an auxiliary input, which replaces the trimap. By designing a network architecture, this method simultaneously achieves the output alpha matte and foreground image, further improving the efficiency of the matting process. Then, [PERSON] et al. [28] modified the network structure based on [21] to achieve real-time video matting and proposed a new idea of using the foreground residual as the prediction target. Experimental results indicate that the utilization of the foreground residual can substantially enhance the convergence rate of the network and effectively address color overflow problems.  
The difficulty of obtaining a background image is much lower than that of creating a trimap; however, usually only the background image can be conveniently obtained in the real world, and when processing videos, this method's effectiveness significantly decreases when the background changes. In addition to using the background to replace the trimap, researchers have designed models where users provide constraint information through interaction. [PERSON] et al. [11] designed a recursive reinforcement learning model that can detect the marked regions in the image to obtain a good alpha matte and can provide feedback for detecting the next suggested marked region based on the foreground and background of user scribbles. Through iteration, the model obtains a fine alpha matte. On the other hand, [PERSON] et al. [23] divided the image into blocks and automatically selected important regions for generating the alpha matte based on the image content. Users scribbled the foreground, background, and transition regions, and the CNN expanded the scribbled information to the entire image to iteratively generate an accurate trimap. These models combine user interaction with traditional matting algorithms to generate the final alpha matte. Unlike the approach of combining interactive information with traditional matting algorithms [11; 23], [PERSON] et al. [38] implemented a CNN architecture that supports arbitrary trimap inputs. In the absence of a trimap input, the model assumes a completely unknown trimap as the input and generates the corresponding foreground and background. The model can also be guided by user scribble markings to correct the generated alpha matte. [PERSON] et al. [32] and [PERSON] [37] et al. used user clicks as auxiliary inputs. [PERSON] et al. [32] marked the foreground and background of the image by utilizing user clicks. The corresponding heat map was generated for the click position and was inputted into the model to generate the alpha matte. In addition to using clicks to mark the foreground and background, [PERSON] et al. [37] also added markings for the unknown area, which would generate the corresponding Euclidean distance map. The model gradually propagated the marking information to adjacent areas through a specific strategy to continuously correct the alpha matte generated by the model.