Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors

• URL: http://arxiv.org/abs/2009.00702v1
• Date: Tue, 1 Sep 2020 21:08:30 GMT
• Title: Unsupervised Single-Image Reflection Separation Using Perceptual Deep Image Priors
• Authors: Suhong Kim, Hamed RahmaniKhezri, Seyed Mohammad Nourbakhsh and Mohamed Hefeeda
• Abstract summary: 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.
• Score: 6.333390830515411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reflections often degrade the quality of the image by obstructing the background scene. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised-learning models. However, these models require an extensive number of image pairs to perform well, especially on natural images with reflection, which is difficult to achieve in practice. In this paper, we propose a novel unsupervised framework for single-image reflection separation. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional networks on a target image to generate two exclusive background and reflection layers. In particular, we design a new architecture of the network to embed semantic features extracted from a pre-trained deep classification network, which gives more meaningful separation similar to human perception. Quantitative and qualitative results on commonly used datasets in the literature show that our method's performance is at least on par with the state-of-the-art supervised methods and, occasionally, better without requiring large training datasets. Our results also show that our method significantly outperforms the closest unsupervised method in the literature for removing reflections from single images.

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)
• Improving Human-Object Interaction Detection via Virtual Image Learning [68.56682347374422]
  Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects. In this paper, we propose to alleviate the impact of such an unbalanced distribution via Virtual Image Leaning (VIL) A novel label-to-image approach, Multiple Steps Image Creation (MUSIC), is proposed to create a high-quality dataset that has a consistent distribution with real images.
  arXiv  Detail & Related papers  (2023-08-04T10:28:48Z)
• Improved Multiple-Image-Based Reflection Removal Algorithm Using Deep Neural Networks [13.16514846876752]
  Reflection of another scene can often be found in a semi-reflective medium such as glass. In this paper, a novel deep neural network approach for solving the reflection problem in imaging is presented.
  arXiv  Detail & Related papers  (2022-08-09T11:55:53Z)
• 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)
• Two-Stage Single Image Reflection Removal with Reflection-Aware Guidance [78.34235841168031]
  We present a novel two-stage network with reflection-aware guidance (RAGNet) for single image reflection removal (SIRR) RAG can be used (i) to mitigate the effect of reflection from the observation, and (ii) to generate mask in partial convolution for mitigating the effect of deviating from linear combination hypothesis. Experiments on five commonly used datasets demonstrate the quantitative and qualitative superiority of our RAGNet in comparison to the state-of-the-art SIRR methods.
  arXiv  Detail & Related papers  (2020-12-02T03:14:57Z)
• 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)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)
• 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.