Single Image Reflection Removal Beyond Linearity

Wen, Quan; Tan, Yushan; Qin, Jing; Liu, Wenxi; Han, Guoqiang; He, Shengfeng

doi:10.1109/cvpr.2019.00389

Cited by 119 publications

(107 citation statements)

References 20 publications

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“…Wan et al [17] applies a multi-scale strategy on the learning network to improve the target details. Wen et al [18] synthesizes and remove reflection with a non-linear model. Perceptual loss functions have been adopted in [4].…”

Section: Related Workmentioning

confidence: 99%

“…We compare our method with state of the art methods [19,18,4,23,24], including single or polarization-based approaches. The implementation is based on both polarization-guided synthetic data and our delicately captured experiment data.…”

Section: Visual Comparison In Synthetic and Real Scenementioning

confidence: 99%

“…The implementation is based on both polarization-guided synthetic data and our delicately captured experiment data. We first show a visual comparison ( [19,18,4]) for synthetic polarized dataset (Fig. 3).…”

Section: Visual Comparison In Synthetic and Real Scenementioning

confidence: 99%

“…Visual comparison for synthetic polarized dataset. From left to right are: polarized images (Polarized ), transmitted scene (GT ), ERRnet[19], Wen et al[18], Zhang et al[4], and our transmitted results (Ours), respectively.…”

mentioning

confidence: 99%

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Reflection Separation via Multi-bounce Polarization State Tracing

Qiu

Zang

et al. 2020

Lecture Notes in Computer Science

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Reflection removal from photographs is an important task in computational photography, but also for computer vision tasks that involve imaging through windows and similar settings. Traditionally, the problem is approached as a single reflection removal problem under very controlled scenarios. In this paper we aim to generalize the reflection removal to real-world scenarios with more complicated light interactions. To this end, we propose a simple yet efficient learning framework for supervised image reflection separation with a polarization-guided raytracing model and loss function design. Instead of a conventional image sensor, we use a polarization sensor that instantaneously captures four linearly polarized photos of the scene in the same image. Through a combination of a new polarization-guided image formation model and a novel supervised learning framework for the interpretation of a raytracing image formation model, a general method is obtained to tackle general image reflection removal problems. We demonstrate our method with extensive experiments on both real and synthetic data and demonstrate the unprecedented quality of image reconstructions.

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Section: Related Workmentioning

confidence: 99%

Section: Visual Comparison In Synthetic and Real Scenementioning

confidence: 99%

Section: Visual Comparison In Synthetic and Real Scenementioning

confidence: 99%

mentioning

confidence: 99%

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Reflection Separation via Multi-bounce Polarization State Tracing

Qiu

Zang

et al. 2020

Lecture Notes in Computer Science

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“…Especially, our method shows a large improvement on the DUTS-test dataset, which includes many challenge cases, demonstrating the strong capability of our PSCNet. In the future, we will explore the potential of our PSC module design for other layer separation tasks, such as mirror detection [79], lane marking detection [80], shadow detection [81]- [83] and removal [84], [85], reflection removal [86], [87], rain removal [88], haze removal [89], [90], etc.…”

Section: Comparison With the State-of-the-artsmentioning

confidence: 99%

Pyramid Spatial Context Features for Salient Object Detection

2020

IEEE Access

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Salient object detection aims to obtain the most attractive objects from the input images, which severs as a pre-processing step for many image processing tasks. This paper presents a novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at multiple scales. To achieve this, we first adopt convolutional operations with different dilated rates to generate the feature maps with different respective fields, and then use the two-round recurrent translations to explore multiple types of spatial context features on these feature maps. By further inserting this module in a deep network, namely PSCNet, we are able to optimize the network in an end-to-end manner for salient object detection. We evaluate the proposed method on six public benchmark datasets by comparing it with 25 salient object detection methods. The experimental results demonstrate that our PSCNet performs favorably against all the other methods. INDEX TERMS Salient object detection, spatial context, pyramid features, deep neural network.

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Zero-Shot Learning for Reflection Removal of Single 360-Degree Image

Han

Sim

2022

Lecture Notes in Computer Science

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Single Image Reflection Removal Beyond Linearity

Cited by 119 publications

References 20 publications

Reflection Separation via Multi-bounce Polarization State Tracing

Reflection Separation via Multi-bounce Polarization State Tracing

Pyramid Spatial Context Features for Salient Object Detection

Zero-Shot Learning for Reflection Removal of Single 360-Degree Image

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