Overcoming visual reverberations

Diamant, Y.; Schechner, Yoav Y.

doi:10.1109/cvpr.2008.4587768

Cited by 34 publications

(27 citation statements)

References 29 publications

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“…Our work uses the ghosting model proposed by Diamant and Schechner [9]. They quantified the ghosting in both the transmission layer T and reflection layer R. In this work, we consider a simplified version of their model, involving only the first-order reflection in R; our model is shown in Figure 3.…”

Section: Ghosting Formation Modelmentioning

confidence: 99%

“…As in [9], we assume that the spatial shift and relative attenuation between R 1 and R 2 is spatially invariant. Based on Fresnel's equations [14], these assumptions hold when the reflection layer does not have large depth variations, and when the angle between camera and glass normal is not too oblique.…”

Section: Ghosting Formation Modelmentioning

confidence: 99%

“…In image processing, the use of the ghosting effect for reflection removal was introduced in [9], and two polarized images were used to achieve layer separation.…”

Section: Related Workmentioning

confidence: 99%

“…As we show later, higher-order reflections carry minimal energy and can be ignored. Given the input image I, our goal is to recover the kernel k, transmission layer T , and reflection layer R. Most previous work (with the exception of [9]), assumed that I = T + R in their imaging models. Solving this ill-posed problem requires either very effective image priors, or auxiliary data such as multiple images captured with motion or polarizers, or user input.…”

Section: Introductionmentioning

confidence: 99%

“…Following the work in Diamant et al [9], we model the kernel k as a two-pulse kernel, parameterized by the distance and the relative intensity between the primary and secondary reflections. As we show later, higher-order reflections carry minimal energy and can be ignored.…”

Section: Introductionmentioning

confidence: 99%

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Reflection removal using ghosting cues

Shih

Krishnan

Durand

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Photographs taken through glass windows often contain both the desired scene and undesired reflections. Separating the reflection and transmission layers is an important but ill-posed problem that has both aesthetic and practical applications. In this work, we introduce the use of ghosting cues that exploit asymmetry between the layers, thereby helping to reduce the ill-posedness of the problem. These cues arise from shifted double reflections of the reflected scene off the glass surface. In double-pane windows, each pane reflects shifted and attenuated versions of objects on the same side of the glass as the camera. For single-pane windows, ghosting cues arise from shifted reflections on the two surfaces of the glass pane. Even though the ghosting is sometimes barely perceptible by humans, we can still exploit the cue for layer separation. In this work, we model the ghosted reflection using a double-impulse convolution kernel, and automatically estimate the spatial separation and relative attenuation of the ghosted reflection components. To separate the layers, we propose an algorithm that uses a Gaussian Mixture Model for regularization. Our method is automatic and requires only a single input image. We demonstrate that our approach removes a large fraction of reflections on both synthetic and real-world inputs.

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Section: Ghosting Formation Modelmentioning

confidence: 99%

Section: Ghosting Formation Modelmentioning

confidence: 99%

“…In image processing, the use of the ghosting effect for reflection removal was introduced in [9], and two polarized images were used to achieve layer separation.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reflection removal using ghosting cues

Shih

Krishnan

Durand

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Separating Reflection and Transmission Images in the Wild

Wieschollek

Gallo

et al. 2018

Lecture Notes in Computer Science

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The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.

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