User-assisted Video Reflection Removal
- URL: http://arxiv.org/abs/2009.03281v1
- Date: Mon, 7 Sep 2020 17:42:40 GMT
- Title: User-assisted Video Reflection Removal
- Authors: Amgad Ahmed, Suhong Kim, Mohamed Elgharib, Mohamed Hefeeda
- Abstract summary: We propose a user-assisted method for video reflection removal.
We rely on both spatial and temporal information and utilize sparse user hints to help improve separation.
Our experiments show that the proposed method successfully removes reflection from video sequences, does not introduce visual distortions, and significantly outperforms the state-of-the-art reflection removal methods in the literature.
- Score: 14.913848002123643
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 reflection
removal. We rely on both spatial and temporal information and utilize sparse
user hints to help improve separation. The key idea of the proposed method is
to use motion cues to separate the background layer from the reflection layer
with minimal user assistance. We show that user-assistance significantly
improves the layer separation results. We implement and evaluate the proposed
method through quantitative and qualitative results on real and synthetic
videos. Our experiments show that the proposed method successfully removes
reflection from video sequences, does not introduce visual distortions, and
significantly outperforms the state-of-the-art reflection removal methods in
the literature.
Related papers
- Towards Flexible Interactive Reflection Removal with Human Guidance [75.38207315080624]
Single image reflection removal is inherently ambiguous, as both the reflection and transmission components requiring separation may follow natural image statistics.
Existing methods attempt to address the issue by using various types of low-level and physics-based cues as sources of reflection signals.
In this paper, we aim to explore a novel flexible interactive reflection removal approach that leverages various forms of sparse human guidance.
arXiv Detail & Related papers (2024-06-03T17:34:37Z) - A Categorized Reflection Removal Dataset with Diverse Real-world Scenes [54.662456878340215]
We construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR)
The dataset is constructed using diverse glass types under various environments to ensure diversity.
We show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection.
arXiv Detail & Related papers (2021-08-07T06:56:57Z) - ReflectNet -- A Generative Adversarial Method for Single Image
Reflection Suppression [0.6980076213134382]
We propose a single image reflection removal method based on context understanding modules and adversarial training.
Our proposed reflection removal method outperforms state-of-the-art methods in terms of PSNR and SSIM on the SIR benchmark dataset.
arXiv Detail & Related papers (2021-05-11T17:33:40Z) - Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for
Single Image Reflection Removal [6.370905925442655]
We propose an iterative gradient encoding network for single image reflection removal.
Our method can remove reflection favorably against the existing state-of-the-art method on all imaging settings.
arXiv Detail & Related papers (2021-03-29T19:29:29Z) - Location-aware Single Image Reflection Removal [54.93808224890273]
This paper proposes a novel location-aware deep learning-based single image reflection removal method.
We use a reflection confidence map as the cues for the network to learn how to encode the reflection information adaptively.
The integration of location information into the network significantly improves the quality of reflection removal results.
arXiv Detail & Related papers (2020-12-13T19:34:35Z) - Unsupervised Single-Image Reflection Separation Using Perceptual Deep
Image Priors [6.333390830515411]
We propose a novel unsupervised framework for single-image reflection separation.
We optimize the parameters of two cross-coupled deep convolutional networks on a target image to generate two exclusive background and reflection layers.
Our results show that our method significantly outperforms the closest unsupervised method in the literature for removing reflections from single images.
arXiv Detail & Related papers (2020-09-01T21:08:30Z) - Learning to See Through Obstructions with Layered Decomposition [117.77024641706451]
We present a learning-based approach for removing unwanted obstructions from moving images.
Our method leverages motion differences between the background and obstructing elements to recover both layers.
We show that the proposed approach learned from synthetically generated data performs well to real images.
arXiv Detail & Related papers (2020-08-11T17:59:31Z) - Polarized Reflection Removal with Perfect Alignment in the Wild [66.48211204364142]
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.
We build a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images.
arXiv Detail & Related papers (2020-03-28T13:29:31Z) - Single image reflection removal via learning with multi-image
constraints [50.54095311597466]
We propose a novel learning-based solution that combines the advantages of the aforementioned approaches and overcomes their drawbacks.
Our algorithm works by learning a deep neural network to optimize the target with joint constraints enhanced among multiple input images.
Our algorithm runs in real-time and state-of-the-art reflection removal performance on real images.
arXiv Detail & Related papers (2019-12-08T06:10:49Z)
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.