Reflection Removal Using Recurrent Polarization-to-Polarization Network

• URL: http://arxiv.org/abs/2402.18178v1
• Date: Wed, 28 Feb 2024 09:08:22 GMT
• Title: Reflection Removal Using Recurrent Polarization-to-Polarization Network
• Authors: Wenjiao Bian, Yusuke Monno, Masatoshi Okutomi
• Abstract summary: We propose a polarization-to-polarization approach that applies polarized images as the inputs and predicts "polarized" reflection and transmission images. Experimental results on a public dataset demonstrate that our method outperforms other state-of-the-art methods.
• Score: 14.97144413954432
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: This paper addresses reflection removal, which is the task of separating reflection components from a captured image and deriving the image with only transmission components. Considering that the existence of the reflection changes the polarization state of a scene, some existing methods have exploited polarized images for reflection removal. While these methods apply polarized images as the inputs, they predict the reflection and the transmission directly as non-polarized intensity images. In contrast, we propose a polarization-to-polarization approach that applies polarized images as the inputs and predicts "polarized" reflection and transmission images using two sequential networks to facilitate the separation task by utilizing the interrelated polarization information between the reflection and the transmission. We further adopt a recurrent framework, where the predicted reflection and transmission images are used to iteratively refine each other. Experimental results on a public dataset demonstrate that our method outperforms other state-of-the-art methods.

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)
• Polarimetric Light Transport Analysis for Specular Inter-reflection [1.537737222790121]
  We propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. We show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection.
  arXiv  Detail & Related papers  (2023-12-07T08:55:28Z)
• Single Image Reflection Removal with Reflection Intensity Prior Knowledge [14.335849624907611]
  We propose a general reflection intensity prior that captures the intensity of the reflection phenomenon. By segmenting images into regional patches, RPEN learns non-uniform reflection prior in an image. We propose Prior-based Reflection Removal Network (PRRN) using a simple transformer U-Net architecture.
  arXiv  Detail & Related papers  (2023-12-06T14:52:11Z)
• Transparent Shape from a Single View Polarization Image [6.18278691318801]
  This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior.
  arXiv  Detail & Related papers  (2022-04-13T12:24:32Z)
• 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)
• 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)
• 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.