Towards Flexible Interactive Reflection Removal with Human Guidance
- URL: http://arxiv.org/abs/2406.01555v1
- Date: Mon, 3 Jun 2024 17:34:37 GMT
- Title: Towards Flexible Interactive Reflection Removal with Human Guidance
- Authors: Xiao Chen, Xudong Jiang, Yunkang Tao, Zhen Lei, Qing Li, Chenyang Lei, Zhaoxiang Zhang,
- Abstract summary: 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.
- Score: 75.38207315080624
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. However, these cues are not universally applicable, since they are only observable in specific capture scenarios. This leads to a significant performance drop when test images do not align with their assumptions. In this paper, we aim to explore a novel flexible interactive reflection removal approach that leverages various forms of sparse human guidance, such as points and bounding boxes, as auxiliary high-level prior to achieve robust reflection removal. However, incorporating the raw user guidance naively into the existing reflection removal network does not result in performance gains. To this end, we innovatively transform raw user input into a unified form -- reflection masks using an Interactive Segmentation Foundation Model. Such a design absorbs the quintessence of the foundational segmentation model and flexible human guidance, thereby mitigating the challenges of reflection separations. Furthermore, to fully utilize user guidance and reduce user annotation costs, we design a mask-guided reflection removal network, comprising our proposed self-adaptive prompt block. This block adaptively incorporates user guidance as anchors and refines transmission features via cross-attention mechanisms. Extensive results on real-world images validate that our method demonstrates state-of-the-art performance on various datasets with the help of flexible and sparse user guidance. Our code and dataset will be publicly available here https://github.com/ShawnChenn/FlexibleReflectionRemoval.
Related papers
- Reflection Invariance Learning for Few-shot Semantic Segmentation [53.20466630330429]
Few-shot semantic segmentation (FSS) aims to segment objects of unseen classes in query images with only a few annotated support images.
This paper proposes a fresh few-shot segmentation framework to mine the reflection invariance in a multi-view matching manner.
Experiments on both PASCAL-$5textiti$ and COCO-$20textiti$ datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2023-06-01T15:14:58Z) - 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) - 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) - User-assisted Video Reflection Removal [14.913848002123643]
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.
arXiv Detail & Related papers (2020-09-07T17:42:40Z) - 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) - 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.