Multi-Branch Neural Network for Trimap-Free Image Matting
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Solution Overview
Problem
Conventional image matting systems face challenges in accurately detecting objects with fuzzy or blurry boundaries in digital images, often requiring trimap segmentations which lengthen processing times and require additional user intervention.
Innovation Solution
A multi-branch neural network system that generates image mattes without trimap segmentations by extracting coarse semantic masks, detail masks, and fusing them to produce an image matte, with a refinement neural network further refining selected portions for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image matting systems use trimap segmentations to detect objects with fuzzy boundaries, then measurement precision is improved, but processing time increases and device complexity increases
Solution Approach 1:
The system enables automatic image matting without requiring user-provided trimap segmentations. The multi-branch neural network autonomously processes the input image, extracting coarse semantic masks, detail masks, and fusing them to generate the final matte, thereby eliminating the need for manual intervention while maintaining high accuracy in detecting fuzzy boundaries
Solution Approach 2:
The neural network is divided into multiple branches that perform specialized functions: a first branch extracts coarse semantic masks, a second branch extracts detail masks, and a third branch fuses these results. This segmentation of the processing task allows each branch to optimize for its specific function while working together to achieve accurate boundary detection without requiring external trimap inputs
2Measurement precision
If conventional image matting systems use trimap segmentations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the functions of trimap segmentation and image matting into a single integrated multi-branch neural network. Instead of requiring separate trimap generation and matting processes, the combined system processes images directly through multiple branches that cooperate to produce the final matte, reducing overall system complexity while maintaining precision
Solution Approach 2:
The multi-branch neural network performs multiple functions within a single system: it extracts coarse semantic information, extracts detailed boundary information, and fuses these results into the final matte. This multi-functionality eliminates the need for separate trimap segmentation tools and processing steps, thereby reducing device complexity
3Productivity
If a multi-branch neural network generates image mattes without trimap segmentations, then productivity is improved, but measurement precision may be compromised
Solution Approach 1:
The neural network segments the matting task into three specialized branches: one for coarse semantic mask extraction, one for detail mask extraction, and one for fusion. This segmentation allows each branch to focus on specific aspects of the problem, achieving both efficiency through parallel processing and precision through specialized processing of different image regions
Solution Approach 2:
The system transitions from a single-dimensional processing approach (requiring trimap input) to a multi-dimensional approach where the neural network processes images through multiple branches operating at different levels of abstraction. The coarse semantic branch handles high-level semantics while the detail branch handles boundary information, and their fusion creates a comprehensive matte that achieves both speed and accuracy
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.