This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering. Unlike most other deep learning strategies applied in this context, our approach tackles these challenging problems by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that no handcrafted or application-specific image-processing components are required. We apply the resulting transferrable pipeline to two different problem domains that are both sensitive to edges, namely, single image reflection removal and image smoothing. For the former, using a mild reflection smoothness assumption and a novel synthetic data generation method that acts as a type of weak supervision, our network is able to solve much more difficult reflection cases that cannot be handled by previous methods. For the latter, we also exceed the state-of-the-art quantitative and qualitative results by wide margins. In all cases, the proposed framework is simple, fast, and easy to transfer across disparate domains.
Beijing Jiaotong University（北京交通大学）；Simon Fraser University（西蒙弗雷泽大学）；Shandong Normal University（山东师范大学）；Taiyuan University of Science and Technology（太原科技大学）
In this paper, two local activity-tuned filtering frameworks are proposed for noise removal and image smoothing, where the local activity measurement is given by the clipped and normalized local variance or standard deviation. The first framework is a modified anisotropic diffusion for noise removal of piece-wise smooth image. The second framework is a local activity-tuned Relative Total Variation (LAT-RTV) method for image smoothing. Both frameworks employ the division of gradient and the local activity measurement to achieve noise removal. In addition, to better capture local information, th…
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…
Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes, because important facial features are completely occluded. In this paper, we propose and solve the face image reflection removal problem. We remove non-transmitted reflections by incorporating inpainting ideas into a guided reflection removal framework and recover facial features by considering various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with s…
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 architectu…
The Hong Kong University of Science and Technology（香港科技大学）
We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. We observe that this flash-only image is visually reflection-free, and thus it can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a…
State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）；Shanghai Jiao Tong University（上海交通大学）；Clemson University（克莱姆森大学）；City University of Hong Kong（香港城市大学）
This paper proposes a novel location-aware deep-learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells if a region is reflection-dominated or transmission-dominated, and it is used as a cue for the network to control the feature flow when predicting the reflection and transmission layers. We design our network as a recurrent network to progressively refine reflection removal results at each iteration. The novelty is th…
Harbin Institute of Technology（哈尔滨工业大学）；Tianjin University（天津大学）
Removing undesired reflection from an image captured through a glass surface is a very challenging problem with many practical application scenarios. For improving reflection removal, cascaded deep models have been usually adopted to estimate the transmission in a progressive manner. However, most existing methods are still limited in exploiting the result in prior stage for guiding transmission estimation. In this paper, we present a novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR). To be specific, the reflection layer is firstly estim…
Adobe Research（Adobe研究）；UC Berkeley（加州大学伯克利分校）；Google Research（谷歌研究）；Portland State University（波特兰州立大学）
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registratio…
The Hong Kong Polytechnic University（香港理工大学）；ETH Zurich（苏黎世联邦理工学院）；Center for Artificial Intelligence and Robotics, HKISI-CAS（中国科学院香港智能科学研究所人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with …
GRAR: Glass-induced Reflection Artifact Removal in LiDAR Point Clouds
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Design, Hunan University（湖南大学设计学院）；School of Finance and Statistics, Hunan University（湖南大学金融与统计学院）
PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；Department of Computer Science at Memorial University of Newfoundland（纪念大学计算机科学系）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；School of Engineering, University of California at Merced（加州大学默塞德分校工程学院）
McMaster University（麦克马斯特大学）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image. While separating the reflection from a familiar object in an image is mentally not difficult for humans, it is a challenging, ill-posed problem in computer vision. In this paper, we propose a novel deep convolutional encoder-decoder method to remove the objectionable reflection by learning a map between image pairs with and without reflection. For training the neural network, we model the physical formation of reflections in images and s…
Huazhong University of Science and Technology（华中科技大学）；Microsoft Research Asia（微软亚洲研究院）；Cornell University（康奈尔大学）
We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterative Boost Convolutional LSTM Network (IBCLN) that enables cascaded prediction for reflection removal. IBCLN is a cascaded network that iteratively refines the estimates of transmission and reflection layers in a manner that they can boost the prediction quality to each other, and i…
University of Cambridge（剑桥大学）；Shandong University（山东大学）；University of Science and Technology of China（中国科学技术大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Removing reflection artefacts from a single image is a problem of both theoretical and practical interest, which still presents challenges because of the massively ill-posed nature of the problem. In this work, we propose a technique based on a novel optimisation problem. Firstly, we introduce a simple user interaction scheme, which helps minimise information loss in reflection-free regions. Secondly, we introduce an $H^2$ fidelity term, which preserves fine detail while enforcing global colour similarity. We show that this combination allows us to mitigate some major drawbacks of the existing…
The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Institute of Science Tokyo（东京科学大学）
Single image reflection removal via learning with multi-image constraints
Peking University（北京大学）；Tencent AI Lab（腾讯人工智能实验室）；Tencent（腾讯）；Shandong University（山东大学）；Microsoft Cloud AI（微软云人工智能）；University of Cambridge（剑桥大学）；National University of Singapore（新加坡国立大学）
Beihang University（北京航空航天大学）；International Digital Economy Academy（国际数字经济学院）；University of Amsterdam（阿姆斯特丹大学）；Peng Cheng Laboratory（鹏城实验室）
Reflection is common in images capturing scenes behind a glass window, which is not only a disturbance visually but also influence the performance of other computer vision algorithms. Single image reflection removal is an ill-posed problem because the color at each pixel needs to be separated into two values, i.e., the desired clear background and the reflection. To solve it, existing methods propose priors such as smoothness, color consistency. However, the low-level priors are not reliable in complex scenes, for instance, when capturing a real outdoor scene through a window, both the foregro…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Colonoscopy video frames might be contaminated by bright spots with unsaturated values known as specular reflection. Detection and removal of such reflections could enhance the quality of colonoscopy images and facilitate diagnosis procedure. In this paper we propose a novel two-phase method for this purpose, consisting of detection and removal phases. In the detection phase, we employ both HSV and RGB color space information for segmentation of specular reflections. We first train a non-linear SVM for selecting a color space based on image statistical features extracted from each channel of t…
Beijing Institute of Technology（北京理工大学）；Microsoft Research（微软研究院）
Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems. Although state-of-the-art methods can obtain decent results in certain situations, performance declines significantly when tackling more general real-world cases. These failures stem from the intrinsic difficulty of single image reflection removal -- the fundamental ill-posedness of the problem, and the insufficiency of densely-labeled training data needed for resolving this ambiguity within learning-based neural network pipelines. In this paper, we addre…
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）
Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）
Recently, multiple synthetic and real-world datasets have been built to facilitate the training of deep single image reflection removal (SIRR) models. Meanwhile, diverse testing sets are also provided with different types of reflection and scenes. However, the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing images. The diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective to all testing sets and real-world reflections. In this paper, we tackle these issue…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
Recently, deep learning-based single image reflection separation methods have been exploited widely. To benefit the learning approach, a large number of training image pairs (i.e., with and without reflections) were synthesized in various ways, yet they are away from a physically-based direction. In this paper, physically based rendering is used for faithfully synthesizing the required training images, and a corresponding network structure and loss term are proposed. We utilize existing RGBD/RGB images to estimate meshes, then physically simulate the light transportation between meshes, glass,…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Reflections in videos are obstructions that often occur when videos are taken behind reflective surfaces like glass. These reflections reduce the quality of such videos, lead to information loss and degrade the accuracy of many computer vision algorithms. A video containing reflections is a combination of background and reflection layers. Thus, reflection removal is equivalent to decomposing the video into two layers. This, however, is a challenging and ill-posed problem as there is an infinite number of valid decompositions. To address this problem, we propose a user-assisted method for video…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）
Removing undesired reflections from a photo taken in front of glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection types. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the n…
When imaging through a semi-reflective medium such as glass, the reflection of another scene can often be found in the captured images. It degrades the quality of the images and affects their subsequent analyses. In this paper, a novel deep neural network approach for solving the reflection problem in imaging is presented. Traditional reflection removal methods not only require long computation time for solving different optimization functions, their performance is also not guaranteed. As array cameras are readily available in nowadays imaging devices, we first suggest in this paper a multiple…
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；College of Engineering and Computer Science, Australian National University（澳大利亚国立大学工程与计算机科学学院）；Renmin University of China（中国人民大学）
Taking pictures through glass windows almost always produces undesired reflections that degrade the quality of the photo. The ill-posed nature of the reflection removal problem reached the attention of many researchers for more than decades. The main challenge of this problem is the lack of real training data and the necessity of generating realistic synthetic data. In this paper, we proposed a single image reflection removal method based on context understanding modules and adversarial training to efficiently restore the transmission layer without reflection. We also propose a complex data ge…
SAOCOM Mission, National Commission of Space Activities (CONAE)（阿根廷国家空间活动委员会（CONAE）SAOCOM任务）
Synthetic aperture radar (SAR) images are subject to prominent speckle noise, which is generally considered a purely multiplicative noise process. In theory, this multiplicative noise is that the ratio of the standard deviation to the signal value, the "coefficient of variation," is theoretically constant at every point in a SAR image. Most of the filters for speckle reduction are based on this property. Such property is irrelevant for the new filter structure, which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR imag…
A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电气与电子工程系）
