Image Matting
96 papers with code • 8 benchmarks • 8 datasets
Image Matting is the process of accurately estimating the foreground object in images and videos. It is a very important technique in image and video editing applications, particularly in film production for creating visual effects. In case of image segmentation, we segment the image into foreground and background by labeling the pixels. Image segmentation generates a binary image, in which a pixel either belongs to foreground or background. However, Image Matting is different from the image segmentation, wherein some pixels may belong to foreground as well as background, such pixels are called partial or mixed pixels. In order to fully separate the foreground from the background in an image, accurate estimation of the alpha values for partial or mixed pixels is necessary.
Source: Automatic Trimap Generation for Image Matting
Image Source: Real-Time High-Resolution Background Matting
Benchmarks
These leaderboards are used to track progress in Image Matting
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Libraries
Use these libraries to find Image Matting models and implementationsDatasets
Composition-1K
AIM-500
Distinctions-646
P3M-10k
PPM-100
withoutbg100 dataset
Most implemented papers
TOM-Net: Learning Transparent Object Matting from a Single Image
guanyingc/TOM-Net
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CVPR 2018
In this paper, we first formulate transparent object matting as a refractive flow estimation problem.
Visual Object Tracking: The Initialisation Problem
This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising their object models in background regions of the BB.
Deep-Energy: Unsupervised Training of Deep Neural Networks
The success of deep learning has been due, in no small part, to the availability of large annotated datasets.
AlphaGAN: Generative adversarial networks for natural image matting
We present the first generative adversarial network (GAN) for natural image matting.
Fourier-Domain Optimization for Image Processing
Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others.
Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing
woshiyyya/Auto-Retoucher-pytorch
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Replacing the background and simultaneously adjusting foreground objects is a challenging task in image editing.
Instance Segmentation based Semantic Matting for Compositing Applications
p0ete/Image-Segmentation-With-Trimap
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In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task.
A Late Fusion CNN for Digital Matting
This paper studies the structure of a deep convolutional neural network to predict the foreground alpha matte by taking a single RGB image as input.
Learning Transparent Object Matting
In this paper, we formulate transparent object matting as a refractive flow estimation problem, and propose a deep learning framework, called TOM-Net, for learning the refractive flow.
Indices Matter: Learning to Index for Deep Image Matting
poppinace/indexnet_matting
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ICCV 2019
We show that existing upsampling operators can be unified with the notion of the index function.
