Single Image Reflection Removal Based on GAN with Gradient Constraint

Abiko, Ryo; Ikehara, Masaaki

doi:10.1007/978-3-030-41404-7_43

Cited by 6 publications

(13 citation statements)

References 26 publications

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“…This data set contains real-world reflection images with diverse backgrounds and imaging conditions. For comparison we considered following state of the art methods, PNet [18], ERRNet [19], GCNet [12], IBCLN [6], CoRRN [13]. For a fair comparison, we have used the codes and trained model provided by the original authors.…”

Section: Methodsmentioning

confidence: 99%

“…To deal with the problem's ill-posedness, the recent learning-based method has utilized different auxiliary information as prior and constraint [8], [9], [10], [11]. Among them, one school of researchers had tried to exploit auxiliary information embedded in the image's gradient [12], [13]. For example, in [9] Wan et al pro-posed to use an auxiliary network to restore the gradient of the corrupted image and fused the features of the gradient network with the image restoration network.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [9] Wan et al pro-posed to use an auxiliary network to restore the gradient of the corrupted image and fused the features of the gradient network with the image restoration network. In a different work, M Ikehara et al [12] proposed a feature space gradient constraint loss. In [13] Ket et al proposed an MMD loss based on the higher-order statics of image gradient.…”

Section: Introductionmentioning

confidence: 99%

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Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal

Bera,

Biswas

2021

Preprint

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Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection type. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR 2 demonstrated that our method can remove reflection favorably against the existing state of the art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal

Bera,

Biswas

2021

Preprint

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“…Our method uses a rotation averaging process that averages multiple output images generated from multiple rotated input images to achieve improved results. We have used the same process in our previous method [28], and this process can be used as a general-purpose process.…”

Section: E Rotate Averaging Processmentioning

confidence: 99%

Channel Attention GAN Trained With Enhanced Dataset for Single-Image Shadow Removal

Abiko

Ikehara

2022

IEEE Access

Self Cite

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Even today, where many deep-learning-based methods have been published, single-image shadow removal is a challenging task to achieve high accuracy. This is because the shadow changes depending on various conditions such as the target material or the light source, and it is difficult to estimate all the physical parameters. In this paper, we propose a new single-image shadow removal method (Channel Attention GAN: CANet) using two networks for detecting shadows and removing shadows. Intensity change in shadowed regions has different characteristics depending on the wavelength of light. In addition, the image acquisition system of the camera acquires an image in a state where the RGB values influence each other. Therefore, our method focused on the physical properties of shadows and the camera's image acquisition system. The proposed network has a structure considering the relationship between color channels. When training this network, we modified the color and added some artifacts to the training images in order to make the training dataset more complex. These image processing are based on the shadow model, considering the camera image acquisition system. With these new proposals, our method can remove shadows in all ISTD, ISTD+, SRD, and SRD+ datasets with higher accuracy than the state-of-the-art methods. The code is available on GitHub: https://github.com/ryo-abiko/CANet.

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“…Therefore, it could be useful to develop methods to obtain tool images with a higher quality by image repair. GAN has been successfully employed to achieve reflection removal [6] and image inpainting [7]. However, as far as we know, the image repair function has not been investigated for surgical tool segmentation.…”

Section: Introductionmentioning

confidence: 99%

Surgical Tool Segmentation Using Generative Adversarial Networks With Unpaired Training Data

Zhang

Rosa

Nageotte

2021

IEEE Robot. Autom. Lett.

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Surgical tool segmentation is a challenging and crucial task for computer and robot-assisted surgery. Supervised learning approaches have shown great success for this task. However, they need a large number of paired training data. Based on Generative Adversarial Networks (GAN), unpaired image-toimage translation (I2I) techniques (like CycleGAN and dualGAN) have been proposed to avoid the requirement of paired data and have been employed for surgical tool segmentation. The unpaired I2I methods avoid annotating images for domain changes. Instead of using them directly for the segmentation task, we propose new GAN-based methods for unpaired I2I by embedding a specific constraint for segmentation, namely each pixel of input image belongs to either background or surgical tool. Our methods simplify the architectures of existing unpaired I2I with a reduced number of generators and discriminators. Compared with dualGAN, the proposed strategies have a faster training process without reducing the accuracy of the segmentation. Besides, we show that, using textured tool images as annotated samples to train discriminators, unpaired I2I (including our methods) can achieve simultaneous tool image segmentation and repair (such as reflection removal and tool inpainting). The proposed strategies are validated for image segmentation of a flexible tool and for in vivo images from the EndoVis dataset.

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Single Image Reflection Removal Based on GAN with Gradient Constraint

Cited by 6 publications

References 26 publications

Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal

Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal

Channel Attention GAN Trained With Enhanced Dataset for Single-Image Shadow Removal

Surgical Tool Segmentation Using Generative Adversarial Networks With Unpaired Training Data

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