We present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets where the collected reflection-free images are not perfectly aligned with input mixed images due to glass refraction. Then we build a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images. Second, capitalizing on the special relationship between reflection and polarized light, we propose a polarized reflection removal model with a two-stage architecture. In addition, we design a novel perceptual NCC loss that can improve the performance of reflection removal and general image decomposition tasks. We conduct extensive experiments, and results suggest that our model outperforms state-of-the-art methods on reflection removal.
CERTH, Information Technologies Institute（CERTH信息技术研究所）；Samsung Research UK（三星英国研究院）
Document shadow removal is an integral task in document enhancement pipelines, as it improves visibility, readability and thus the overall quality. Assuming that the majority of practical document shadow removal scenarios require real-time, accurate models that can produce high-resolution outputs in-the-wild, we propose Laplacian Pyramid with Input/Output Attention Network (LP-IOANet), a novel pipeline with a lightweight architecture and an upsampling module. Furthermore, we propose three new datasets which cover a wide range of lighting conditions, images, shadow shapes and viewpoints. Our re…
Beyond Shadows: A Large-Scale Benchmark and Multi-Stage Framework for High-Fidelity Facial Shadow Removal
New York Institute of Technology（纽约理工学院）；Shanghai University（上海大学）；Central South University（中南大学）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Huizhou University（惠州大学）
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is…
Unified Removal of Raindrops and Reflections: A New Benchmark and A Novel Pipeline
School of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院）；Huanjiang Laboratory（浣江实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州研究院）；The Chinese University of Hong Kong（香港中文大学）
Eyeglasses removal is challenging in removing different kinds of eyeglasses, e.g., rimless glasses, full-rim glasses and sunglasses, and recovering appropriate eyes. Due to the large visual variants, the conventional methods lack scalability. Most existing works focus on the frontal face images in the controlled environment, such as the laboratory, and need to design specific systems for different eyeglass types. To address the limitation, we propose a unified eyeglass removal model called Eyeglasses Removal Generative Adversarial Network (ERGAN), which could handle different types of glasses …
Marior: Margin Removal and Iterative Content Rectification for Document Dewarping in the Wild
South China University of Technology（华南理工大学）；Peng Cheng Laboratory（鹏城实验室）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））；IntSig Information Co. Ltd（合合信息科技股份有限公司）
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multi-modal multi-temporal cloud removal model. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system significantly outperforms baselines by 8dB in PSNR. We also demonstrate use cases of our system in digital agricul…
Image manipulation and forgery detection have been a topic of research for more than a decade now. New-age tools and large-scale social platforms have given space for manipulated media to thrive. These media can be potentially dangerous and thus innumerable methods have been designed and tested to prove their robustness in detecting forgery. However, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only with particular datasets. In this work, we analyze the issue of out-of-distribution generalisability of the current st…
Stony Brook University（石溪大学）；Computer Vision Center（计算机视觉中心）；Universitat Autònoma de Barcelona（巴塞罗那自治大学）
Automatic document content processing is affected by artifacts caused by the shape of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic …
理解图像中的阴影效果对于各种视觉和图形问题至关重要，包括内在图像分解，阴影消除，图像重新照明和逆渲染。与其他视觉任务一样，机器学习是一种很有前途的理解阴影的方法，但现实世界的图像几乎没有真实的阴影数据。我们介绍了Shading Annotations in the Wild（SAW），这是一个新的室内场景中阴影注释的大规模公共数据集，包括通过众包获得的多种形式的阴影判断，以及从RGB-D图像自动生成的阴影注释。我们使用这些数据来训练一个卷积神经网络，以预测图像中每个像素的阴影信息。我们展示了我们的数据和网络在应用程序中的内在图像，在那里我们可以减少现有算法产生的分解文物的价值。我们的数据库可以在http://opensurfaces.cs.cornell.edu/saw/上找到。
Beihang University（北京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；National University of Singapore（新加坡国立大学）；Sea AI Lab（Sea人工智能实验室）
In this paper, we address the makeup transfer and removal tasks simultaneously, which aim to transfer the makeup from a reference image to a source image and remove the makeup from the with-makeup image respectively. Existing methods have achieved much advancement in constrained scenarios, but it is still very challenging for them to transfer makeup between images with large pose and expression differences, or handle makeup details like blush on cheeks or highlight on the nose. In addition, they are hardly able to control the degree of makeup during transferring or to transfer a specified part…
BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes
Nankai International Advanced Research Institute (SHENZHEN·FUTIAN)（南开国际先进研究院（深圳·福田））；Peng Cheng Laboratory（鹏城实验室）；College of Computer Science, Nankai University（南开大学计算机学院）；The Hong Kong Polytechnic University（香港理工大学）；OPPO Research Institute（OPPO研究院）
Current multi-category Multiple Object Tracking (MOT) metrics use class labels to group tracking results for per-class evaluation. Similarly, MOT methods typically only associate objects with the same class predictions. These two prevalent strategies in MOT implicitly assume that the classification performance is near-perfect. However, this is far from the case in recent large-scale MOT datasets, which contain large numbers of classes with many rare or semantically similar categories. Therefore, the resulting inaccurate classification leads to sub-optimal tracking and inadequate benchmarking o…
Reconstructing the underlying 3D surface of an object from a single image is a challenging problem that has received extensive attention from the computer vision community. Many learning-based approaches tackle this problem by learning a 3D shape prior from either ground truth 3D data or multi-view observations. To achieve state-of-the-art results, these methods assume that the objects are specified with respect to a fixed canonical coordinate frame, where instances of the same category are perfectly aligned. In this work, we present a new method for joint category-specific 3D reconstruction a…
School of Electronic and Information Engineering（电子与信息工程学院）；South China University of Technology（华南理工大学）
A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and th…
Northwood High School（诺斯伍德高中）；Irvine Unified School District（尔湾联合学区）；Fowler School of Engineering（福勒工程学院）；Chapman University（查普曼大学）
Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which including three conventional machine learning methods, support vector machine, random fo…
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Aalborg University（奥尔堡大学）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Milestone Systems（里程碑系统公司）；Pioneer Center for Artificial Intelligence（先锋人工智能中心）
