Face images captured through the glass are usually contaminated by reflections. The non-transmitted reflections make the reflection removal more challenging than for general scenes, because important facial features are completely occluded. In this paper, we propose and solve the face image reflection removal problem. We remove non-transmitted reflections by incorporating inpainting ideas into a guided reflection removal framework and recover facial features by considering various face-specific priors. We use a newly collected face reflection image dataset to train our model and compare with state-of-the-art methods. The proposed method shows advantages in estimating reflection-free face images for improving face recognition.
Previous works on reflection removal can be roughly classified into two categories. The first category solves by using the non-learning based methods. For example, Li al @cite_10 and Nikolas al @cite_0 made use of the different blur levels of the background and reflection layers. Shih al @cite_37 used the GMM patch prior to remove reflections with the visible ghosting effects. The handcrafted priors adopted by these methods are based on the observations of some special properties between the background and reflection ( , different blur levels @cite_18 @cite_10 ) which is often violated in the general scenes especially when these properties are weakly observed.
@cite_0 @cite_37 @cite_10 @cite_18
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Reflection removal aims at separating the mixture of the desired scene and the undesired reflections. Locating reflection and background edges is a key step for reflection removal. In this paper, we present a visual depth guided method to remove reflections. Our idea is to use Depth of Field (DoF) to label the background and reflection edges. We propose a DoF confidence map where pixels with higher DoF values are assumed to belong to the desired background components. Moreover, we observe that images with different resolutions show different properties in the DoF map. Thus, we introduce a multi-scale DoF computing strategy to classify edge pixels more efficiently. Based on the results of edge classification, the background and reflection layers can be separated. Experimental results validate the effectiveness of our method using real-world photos. Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle this problem in a unified framework. Our proposed network integrates image appearance information and multi-scale gradient information with human perception inspired loss function, and is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Extensive experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods. Reflections are a common artifact in images taken through glass windows. Automatically removing the reflection artifacts after the picture is taken is an ill-posed problem. Attempts to solve this problem using optimization schemes therefore rely on various prior assumptions from the physical world. Instead of removing reflections from a single image, which has met with limited success so far, we propose a novel approach to suppress reflections. It is based on a Laplacian data fidelity term and an l-zero gradient sparsity term imposed on the output. With experiments on artificial and real-world images we show that our reflection suppression method performs better than the state-of-the-art reflection removal techniques. 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.