Deep Image Matting: A Comprehensive Survey
- URL: http://arxiv.org/abs/2304.04672v1
- Date: Mon, 10 Apr 2023 15:48:55 GMT
- Title: Deep Image Matting: A Comprehensive Survey
- Authors: Jizhizi Li, Jing Zhang, Dacheng Tao
- Abstract summary: This paper presents a review of recent advancements in image matting in the era of deep learning.
We focus on two fundamental sub-tasks: auxiliary input-based image matting and automatic image matting.
We discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research.
- Score: 85.77905619102802
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image matting refers to extracting precise alpha matte from natural images,
and it plays a critical role in various downstream applications, such as image
editing. Despite being an ill-posed problem, traditional methods have been
trying to solve it for decades. The emergence of deep learning has
revolutionized the field of image matting and given birth to multiple new
techniques, including automatic, interactive, and referring image matting. This
paper presents a comprehensive review of recent advancements in image matting
in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary
input-based image matting, which involves user-defined input to predict the
alpha matte, and automatic image matting, which generates results without any
manual intervention. We systematically review the existing methods for these
two tasks according to their task settings and network structures and provide a
summary of their advantages and disadvantages. Furthermore, we introduce the
commonly used image matting datasets and evaluate the performance of
representative matting methods both quantitatively and qualitatively. Finally,
we discuss relevant applications of image matting and highlight existing
challenges and potential opportunities for future research. We also maintain a
public repository to track the rapid development of deep image matting at
https://github.com/JizhiziLi/matting-survey.
Related papers
- Negative Results of Image Processing for Identifying Duplicate Questions on Stack Overflow [2.2667044928324747]
We investigated image-based techniques for identifying duplicate questions on Stack Overflow.
We implemented two methods of image analysis: first, integrating the text from images into the question text, and second, evaluating the images based on their visual content using image captions.
Our work lays the foundation for easy replication and hypothesis validation, allowing future research to build upon our approach.
arXiv Detail & Related papers (2024-07-08T00:14:21Z) - Deep Image Deblurring: A Survey [165.32391279761006]
Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image.
Recent advances in deep learning have led to significant progress in solving this problem.
arXiv Detail & Related papers (2022-01-26T01:31:30Z) - Deep Automatic Natural Image Matting [82.56853587380168]
Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap.
We propose a novel end-to-end matting network, which can predict a generalized trimap for any image of the above types as a unified semantic representation.
Our network trained on available composite matting datasets outperforms existing methods both objectively and subjectively.
arXiv Detail & Related papers (2021-07-15T10:29:01Z) - Smart Scribbles for Image Mating [90.18035889903909]
We propose an interactive framework, referred to as smart scribbles, to guide users to draw few scribbles on the input images.
It infers the most informative regions of an image for drawing scribbles to indicate different categories.
It then spreads these scribbles to the rest of the image via our well-designed two-phase propagation.
arXiv Detail & Related papers (2021-03-31T13:30:49Z) - Deep Image Compositing [93.75358242750752]
We propose a new method which can automatically generate high-quality image composites without any user input.
Inspired by Laplacian pyramid blending, a dense-connected multi-stream fusion network is proposed to effectively fuse the information from the foreground and background images.
Experiments show that the proposed method can automatically generate high-quality composites and outperforms existing methods both qualitatively and quantitatively.
arXiv Detail & Related papers (2020-11-04T06:12:24Z) - Bridging Composite and Real: Towards End-to-end Deep Image Matting [88.79857806542006]
We study the roles of semantics and details for image matting.
We propose a novel Glance and Focus Matting network (GFM), which employs a shared encoder and two separate decoders.
Comprehensive empirical studies have demonstrated that GFM outperforms state-of-the-art methods.
arXiv Detail & Related papers (2020-10-30T10:57:13Z) - High-Resolution Deep Image Matting [39.72708676319803]
HDMatt is a first deep learning based image matting approach for high-resolution inputs.
Our proposed method sets new state-of-the-art performance on Adobe Image Matting and AlphaMatting benchmarks.
arXiv Detail & Related papers (2020-09-14T17:53:15Z) - AlphaNet: An Attention Guided Deep Network for Automatic Image Matting [0.0]
We propose an end to end solution for image matting i.e. high-precision extraction of foreground objects from natural images.
We propose a method that assimilates semantic segmentation and deep image matting processes into a single network to generate semantic mattes.
We also construct a fashion e-commerce focused dataset with high-quality alpha mattes to facilitate the training and evaluation for image matting.
arXiv Detail & Related papers (2020-03-07T17:25:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.