Revisiting Single Image Reflection Removal In the Wild

• URL: http://arxiv.org/abs/2311.17320v1
• Date: Wed, 29 Nov 2023 02:31:10 GMT
• Title: Revisiting Single Image Reflection Removal In the Wild
• Authors: Yurui Zhu, Xueyang Fu, Peng-Tao Jiang, Hao Zhang, Qibin Sun, Jinwei Chen, Zheng-Jun Zha, Bo Li
• Abstract summary: This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios. We develop a large-scale, high-quality reflection dataset named Reflection Removal in the Wild (RRW)
• Score: 83.42368937164473
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process, we develop a large-scale, high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14,950 high-resolution real-world reflection pairs, a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations, we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation, drawn from the aligned pairs, leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this, we design a reflection location-aware cascaded framework, specifically tailored for SIRR. Powered by these innovative techniques, our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets will be publicly available.

Related papers

• Planar Reflection-Aware Neural Radiance Fields [32.709468082010126]
  We introduce a reflection-aware NeRF that jointly models planar reflectors, such as windows, and explicitly casts reflected rays to capture the source of the high-frequency reflections. Rendering along the primary ray results in a clean, reflection-free view, while explicitly rendering along the reflected ray allows us to reconstruct highly detailed reflections.
  arXiv  Detail & Related papers  (2024-11-07T18:55:08Z)
• Monocular Identity-Conditioned Facial Reflectance Reconstruction [71.90507628715388]
  Existing methods rely on a large amount of light-stage captured data to learn facial reflectance models. We learn the reflectance prior in image space rather than UV space and present a framework named ID2Reflectance. Our framework can directly estimate the reflectance maps of a single image while using limited reflectance data for training.
  arXiv  Detail & Related papers  (2024-03-30T09:43:40Z)
• 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)
• 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)
• 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)
• 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.