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Dual-context aggregation for universal image matting
Natural image matting aims to estimate the alpha matte of the foreground from a given image. Various approaches have been explored to address this...
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Natural Image Matting with Attended Global Context
Image matting is to estimate the opacity of foreground objects from an image. A few deep learning based methods have been proposed for image matting...
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Micro-scale searching algorithm for high-resolution image matting
Natural image matting based on pixel pair optimization is commonly employed during image post-processing. However, obtaining high-quality alpha...
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Mutli-focus image fusion based on guided filter and image matting network
The problem of limited depth-of-field is one of the major disadvantages in optical imaging devices, whereas multifocus image fusion(MFIF), as an...
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Semantic Image Matting: General and Specific Semantics
Although conventional matting formulation can separate foreground from background in fractional occupancy which can be caused by highly transparent...
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Deep image matting with cross-layer contextual information propagation
Natural image matting focuses on accurately estimating the opacity of the foreground object in an arbitrary background. Recently, deep learning-based...
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Frequency Information Matters for Image Matting
Image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only...
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Weakly Supervised Image Matting via Patch Clustering
Image matting aims to extract the accurate foreground opacity mask for a given image. State-of-the-art approaches are usually based on...
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Deep portrait matting via double-grained segmentation
Portrait matting is an image processing technology that takes the portrait in the image as the foreground and accurately extracts it, and it is...
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Alpha Local Difference Loss Function for Deep Image Matting
In recent years, deep learning-based matting methods have received increasing attention due to their superior performance. The design of the loss...
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Bridging Composite and Real: Towards End-to-End Deep Image Matting
Extracting accurate foregrounds from natural images benefits many downstream applications such as film production and augmented reality. However, the...
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Portrait matting using an attention-based memory network
We propose a novel network to perform auxiliary-free video matting task. Unlike most existing approaches that require trimaps or pre-captured...
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Temporal-spatial information mining and aggregation for video matting
In previous video matting methods, there are some problems that require additional auxiliary information and lack of temporal consistency. To solve...
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Rethinking Portrait Matting with Privacy Preserving
Recently, there has been an increasing concern about the privacy issue raised by identifiable information in machine learning. However, previous...
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Lightweight Image Matting via Efficient Non-local Guidance
Natural image matting aims to estimate the opacity of foreground objects. Most existing approaches involve prohibitive parameters, daunting...
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Local complexity difference matting based on weight map and alpha mattes
Image matting is an essential image processing technology in computer vision with significant diverse practical applications, including image...
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Deep Video Matting with Temporal Consistency
Temporal consistency is a significant issue for video matting. In this study, we propose a temporal feature enhancement network for video matting. To...
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Efficient Semantic-Guidance High-Resolution Video Matting
Video matting has made significant progress in trimap-based field. However, researchers are increasingly interested in auxiliary-free matting because...
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Robust Human Matting via Semantic Guidance
Automatic human matting is highly desired for many real applications. We investigate recent human matting methods and show that common bad cases...
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Alpha matting for portraits using encoder-decoder models
Image matting is a technique used to extract the foreground and background from a given image. In the past, classical algorithms based on sampling,...
