Deep Image Matting: A Comprehensive Survey

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

User-assisted intrinsic images

For many computational photography applications, the lighting and materials in the scene are critical pieces of information. We seek to obtain intrinsic images, which decompose a photo into the product of an illumination component that represents lighting effects and a reflectance component that is the color of the observed material. This is an under-constrained problem and automatic methods are challenged by complex natural images. We describe a new approach that enables users to guide an optimization with simple indications such as regions of constant reflectance or illumination. Based on a simple assumption on local reflectance distributions, we derive a new propagation energy that enables a closed form solution using linear least-squares. We achieve fast performance by introducing a novel downsampling that preserves local color distributions. We demonstrate intrinsic image decomposition on a variety of images and show applications.National Science Foundation (U.S.) (NSF CAREER award 0447561)Institut national de recherche en informatique et en automatique (France) (Associate Research Team “Flexible Rendering”)Microsoft Research (New Faculty Fellowship)Alfred P. Sloan Foundation (Research Fellowship)Quanta Computer, Inc. (MIT-Quanta T Party

ImageSpirit: Verbal Guided Image Parsing

Humans describe images in terms of nouns and adjectives while algorithms operate on images represented as sets of pixels. Bridging this gap between how humans would like to access images versus their typical representation is the goal of image parsing, which involves assigning object and attribute labels to pixel. In this paper we propose treating nouns as object labels and adjectives as visual attribute labels. This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images. We propose an efficient (interactive time) solution. Using the extracted labels as handles, our system empowers a user to verbally refine the results. This enables hands-free parsing of an image into pixel-wise object/attribute labels that correspond to human semantics. Verbally selecting objects of interests enables a novel and natural interaction modality that can possibly be used to interact with new generation devices (e.g. smart phones, Google Glass, living room devices). We demonstrate our system on a large number of real-world images with varying complexity. To help understand the tradeoffs compared to traditional mouse based interactions, results are reported for both a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit

Perceptually inspired image estimation and enhancement

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.Includes bibliographical references (p. 137-144).In this thesis, we present three image estimation and enhancement algorithms inspired by human vision. In the first part of the thesis, we propose an algorithm for mapping one image to another based on the statistics of a training set. Many vision problems can be cast as image mapping problems, such as, estimating reflectance from luminance, estimating shape from shading, separating signal and noise, etc. Such problems are typically under-constrained, and yet humans are remarkably good at solving them. Classic computational theories about the ability of the human visual system to solve such under-constrained problems attribute this feat to the use of some intuitive regularities of the world, e.g., surfaces tend to be piecewise constant. In recent years, there has been considerable interest in deriving more sophisticated statistical constraints from natural images, but because of the high-dimensional nature of images, representing and utilizing the learned models remains a challenge. Our techniques produce models that are very easy to store and to query. We show these techniques to be effective for a number of applications: removing noise from images, estimating a sharp image from a blurry one, decomposing an image into reflectance and illumination, and interpreting lightness illusions. In the second part of the thesis, we present an algorithm for compressing the dynamic range of an image while retaining important visual detail. The human visual system confronts a serious challenge with dynamic range, in that the physical world has an extremely high dynamic range, while neurons have low dynamic ranges.(cont.) The human visual system performs dynamic range compression by applying automatic gain control, in both the retina and the visual cortex. Taking inspiration from that, we designed techniques that involve multi-scale subband transforms and smooth gain control on subband coefficients, and resemble the contrast gain control mechanism in the visual cortex. We show our techniques to be successful in producing dynamic-range-compressed images without compromising the visibility of detail or introducing artifacts. We also show that the techniques can be adapted for the related problem of "companding", in which a high dynamic range image is converted to a low dynamic range image and saved using fewer bits, and later expanded back to high dynamic range with minimal loss of visual quality. In the third part of the thesis, we propose a technique that enables a user to easily localize image and video editing by drawing a small number of rough scribbles. Image segmentation, usually treated as an unsupervised clustering problem, is extremely difficult to solve. With a minimal degree of user supervision, however, we are able to generate selection masks with good quality. Our technique learns a classifier using the user-scribbled pixels as training examples, and uses the classifier to classify the rest of the pixels into distinct classes. It then uses the classification results as per-pixel data terms, combines them with a smoothness term that respects color discontinuities, and generates better results than state-of-art algorithms for interactive segmentation.by Yuanzhen Li.Ph.D

강인한 대화형 영상 분할 알고리즘을 위한 시드 정보 확장 기법에 대한 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 이경무.Segmentation of an area corresponding to a desired object in an image is essential to computer vision problems. This is because most algorithms are performed in semantic units when interpreting or analyzing images. However, segmenting the desired object from a given image is an ambiguous issue. The target object varies depending on user and purpose. To solve this problem, an interactive segmentation technique has been proposed. In this approach, segmentation was performed in the desired direction according to interaction with the user. In this case, seed information provided by the user plays an important role. If the seed provided by a user contain abundant information, the accuracy of segmentation increases. However, providing rich seed information places much burden on the users. Therefore, the main goal of the present study was to obtain satisfactory segmentation results using simple seed information. We primarily focused on converting the provided sparse seed information to a rich state so that accurate segmentation results can be derived. To this end, a minimum user input was taken and enriched it through various seed enrichment techniques. A total of three interactive segmentation techniques was proposed based on: (1) Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type comprised expansion of area around a seed, generation of new seed in a new position, and attention to semantic information. First, in seed expansion, we expanded the scope of the seed. We integrated reliable pixels around the initial seed into the seed set through an expansion step composed of two stages. Through the extended seed covering a wider area than the initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed generation, we created a seed at a new point, but not around the seed. We trained the system by imitating the user behavior through providing a new seed point in the erroneous region. By learning the user's intention, our model could e ciently create a new seed point. The generated seed helped segmentation and could be used as additional information for weakly supervised learning. Finally, through seed attention, we put semantic information in the seed. Unlike the previous models, we integrated both the segmentation process and seed enrichment process. We reinforced the seed information by adding semantic information to the seed instead of spatial expansion. The seed information was enriched through mutual attention with feature maps generated during the segmentation process. The proposed models show superiority compared to the existing techniques through various experiments. To note, even with sparse seed information, our proposed seed enrichment technique gave by far more accurate segmentation results than the other existing methods.영상에서 원하는 물체 영역을 잘라내는 것은 컴퓨터 비전 문제에서 필수적인 요소이다. 영상을 해석하거나 분석할 때, 대부분의 알고리즘들이 의미론적인 단위 기반으로 동작하기 때문이다. 그러나 영상에서 물체 영역을 분할하는 것은 모호한 문제이다. 사용자와 목적에 따라 원하는 물체 영역이 달라지기 때문이다. 이를 해결하기 위해 사용자와의 교류를 통해 원하는 방향으로 영상 분할을 진행하는 대화형 영상 분할 기법이 사용된다. 여기서 사용자가 제공하는 시드 정보가 중요한 역할을 한다. 사용자의 의도를 담고 있는 시드 정보가 정확할수록 영상 분할의 정확도도 증가하게 된다. 그러나 풍부한 시드 정보를 제공하는 것은 사용자에게 많은 부담을 주게 된다. 그러므로 간단한 시드 정보를 사용하여 만족할만한 분할 결과를 얻는 것이 주요 목적이 된다. 우리는 제공된 희소한 시드 정보를 변환하는 작업에 초점을 두었다. 만약 시드 정보가 풍부하게 변환된다면 정확한 영상 분할 결과를 얻을 수 있기 때문이다. 그러므로 본 학위 논문에서는 시드 정보를 풍부하게 하는 기법들을 제안한다. 최소한의 사용자 입력을 가정하고 이를 다양한 시드 확장 기법을 통해 변환한다. 우리는 시드 확대, 시드 생성, 시드 주의 집중에 기반한 총 세 가지의 대화형 영상 분할 기법을 제안한다. 각각 시드 주변으로의 영역 확대, 새로운 지점에 시드 생성, 의미론적 정보에 주목하는 형태의 시드 확장 기법을 사용한다. 먼저 시드 확대에 기반한 기법에서 우리는 시드의 영역 확장을 목표로 한다. 두 단계로 구성된 확대 과정을 통해 처음 시드 주변의 비슷한 픽셀들을 시드 영역으로 편입한다. 이렇게 확장된 시드를 사용함으로써 시드의 희소함과 불균형으로 인한 문제를 해결할 수 있다. 다음으로 시드 생성에 기반한 기법에서 우리는 시드 주변이 아닌 새로운 지점에 시드를 생성한다. 우리는 오차가 발생한 영역에 사용자가 새로운 시드를 제공하는 동작을 모방하여 시스템을 학습하였다. 사용자의 의도를 학습함으로써 효과적으로 시드를 생성할 수 있다. 생성된 시드는 영상 분할의 정확도를 높일 뿐만 아니라 약지도학습을 위한 데이터로써 활용될 수 있다. 마지막으로 시드 주의 집중을 활용한 기법에서 우리는 의미론적 정보를 시드에 담는다. 기존에 제안한 기법들과 달리 영상 분할 동작과 시드 확장 동작이 통합된 모델을 제안한다. 시드 정보는 영상 분할 네트워크의 특징맵과 상호 교류하며 그 정보가 풍부해진다. 제안한 모델들은 다양한 실험을 통해 기존 기법 대비 우수한 성능을 기록하였다. 특히 시드가 부족한 상황에서 시드 확장 기법들은 훌륭한 대화형 영상 분할 성능을 보였다.1 Introduction 1 1.1 Previous Works 2 1.2 Proposed Methods 4 2 Interactive Segmentation with Seed Expansion 9 2.1 Introduction 9 2.2 Proposed Method 12 2.2.1 Background 13 2.2.2 Pyramidal RWR 16 2.2.3 Seed Expansion 19 2.2.4 Re nement with Global Information 24 2.3 Experiments 27 2.3.1 Dataset 27 2.3.2 Implement Details 28 2.3.3 Performance 29 2.3.4 Contribution of Each Part 30 2.3.5 Seed Consistency 31 2.3.6 Running Time 33 2.4 Summary 34 3 Interactive Segmentation with Seed Generation 37 3.1 Introduction 37 3.2 Related Works 40 3.3 Proposed Method 41 3.3.1 System Overview 41 3.3.2 Markov Decision Process 42 3.3.3 Deep Q-Network 46 3.3.4 Model Architecture 47 3.4 Experiments 48 3.4.1 Implement Details 48 3.4.2 Performance 49 3.4.3 Ablation Study 53 3.4.4 Other Datasets 55 3.5 Summary 58 4 Interactive Segmentation with Seed Attention 61 4.1 Introduction 61 4.2 Related Works 64 4.3 Proposed Method 65 4.3.1 Interactive Segmentation Network 65 4.3.2 Bi-directional Seed Attention Module 67 4.4 Experiments 70 4.4.1 Datasets 70 4.4.2 Metrics 70 4.4.3 Implement Details 71 4.4.4 Performance 71 4.4.5 Ablation Study 76 4.4.6 Seed enrichment methods 79 4.5 Summary 82 5 Conclusions 87 5.1 Summary 89 Bibliography 90 국문초록 103Docto

ROAM: a Rich Object Appearance Model with Application to Rotoscoping

Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines. While pixel-wise segmentation techniques can help for this task, professional rotoscoping tools rely on parametric curves that offer the artists a much better interactive control on the definition, editing and manipulation of the segments of interest. Sticking to this prevalent rotoscoping paradigm, we propose a novel framework to capture and track the visual aspect of an arbitrary object in a scene, given a first closed outline of this object. This model combines a collection of local foreground/background appearance models spread along the outline, a global appearance model of the enclosed object and a set of distinctive foreground landmarks. The structure of this rich appearance model allows simple initialization, efficient iterative optimization with exact minimization at each step, and on-line adaptation in videos. We demonstrate qualitatively and quantitatively the merit of this framework through comparisons with tools based on either dynamic segmentation with a closed curve or pixel-wise binary labelling

Towards Generalizable Deep Image Matting: Decomposition, Interaction, and Merging

Image matting refers to extracting the precise alpha mattes from images, playing a critical role in many downstream applications. Despite extensive attention, key challenges persist and motivate the research presented in this thesis. One major challenge is the reliance of auxiliary inputs in previous methods, hindering real-time practicality. To address this, we introduce fully automatic image matting by decomposing the task into high-level semantic segmentation and low-level details matting. We then incorporate plug-in modules to enhance the interaction between the sub-tasks through feature integration. Furthermore, we propose an attention-based mechanism to guide the matting process through collaboration merging. Another challenge lies in limited matting datasets, resulting in reliance on composite images and inferior performance on images in the wild. In response, our research proposes a composition route to mitigate the discrepancies and result in remarkable generalization ability. Additionally, we construct numerous large datasets of high-quality real-world images with manually labeled alpha mattes, providing a solid foundation for training and evaluation. Moreover, our research uncovers new observations that warrant further investigation. Firstly, we systematically analyze and address privacy issues that have been neglected in previous portrait matting research. Secondly, we explore the adaptation of automatic matting methods to non-salient or transparent categories beyond salient ones. Furthermore, we collaborate with language modality to achieve a more controllable matting process, enabling specific target selection at a low cost. To validate our studies, we conduct extensive experiments and provide all codes and datasets through the link (https://github.com/JizhiziLi/). We believe that the analyses, methods, and datasets presented in this thesis will offer valuable insights for future research endeavors in the field of image matting