Image matting plays a key role in image and video editing and composition. Although existing deep learning approaches can produce acceptable image matting results, their performance suffers in real-world applications, where the input images are mostly high resolution. To address this, a group of researchers from UIUC, Adobe Research and the University of Oregon have proposed HDMatt, the first deep learning-based image matting approach for high-resolution image inputs.
Generally, deep learning approaches take an entire input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such methods however may fail when dealing with high-resolution input images in sizes of 5000×5000 pixels or higher due to hardware limitations.
The researchers designed HDMatt to crop an input image and trimap into patches, then estimate the alpha values of each patch. Considering the information loss while only using a single patch and the prediction inconsistency between different patches, HDMatt introduces a novel Cross-Patch Context module (CPC) to effectively leverage cross-patch information for each query (current) patch. The estimated alpha values of each patch are then stitched together to output the final alpha matte of the entire image.
The team tested HDMatt’s capability using the Adobe Image Matting (AIM) and AlphaMatting benchmarks, where its quantitative results were all superior to existing SOTA approaches.
The team also conducted comparative evaluations with SOTA image matting methods IndexNet and ContexNet using input images with resolutions of up to 6000×6000 pixels, in which HDMatt was able to extract finer and more accurate details.
The paper High-Resolution Deep Image Matting is on Arxiv. Notably, second author Ning Xu from Adobe Research was first author on the 2017 paper Deep Image Matting.
Analyst: Victor Lu | Editor: Michael Sarazen; Fangyu Cai
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