Fast depth image denoising and enhancement using a deep convolutional network

Zhang, Xin; Wu, Ruiyuan

doi:10.1109/icassp.2016.7472127

Cited by 52 publications

(35 citation statements)

References 9 publications

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“…We use the method in [18] as the generator to gain the mapping of watermarked images to clean images. The applied convolutional neural network (CNN) can efficiently and flexibly mine deep features of images by combining residual learning and batch normalization (BN).…”

Section: The Architecture Of Generator Gmentioning

confidence: 99%

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Defeating data hiding in social networks using generative adversarial network

Wang

Qian

Feng

et al. 2020

J Image Video Proc.

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As a large number of images are transmitted through social networks every moment, terrorists may hide data into images to convey secret data. Various types of images are mixed up in the social networks, and it is difficult for the servers of social networks to detect whether the images are clean. To prevent the illegal communication, this paper proposes a method of defeating data hiding by removing the secret data without impacting the original media content. The method separates the clean images from illegal images using the generative adversarial network (GAN), in which a deep residual network is used as a generator. Therefore, hidden data can be removed and the quality of the processed images can be well maintained. Experimental results show that the proposed method can prevent secret transmission effectively and preserve the processed images with high quality.

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Section: The Architecture Of Generator Gmentioning

confidence: 99%

“…As most data-hiding methods can be viewed as adding noises, it would be useful to remove the hidden data by image denoising. Although many methods in [16][17][18][19][20] can offer better denoising performances than traditional methods, they are not good at removing the hidden data, esp. the date hidden by robust information-hiding tools.…”

Section: Introductionmentioning

confidence: 99%

Defeating data hiding in social networks using generative adversarial network

Wang

Qian

Feng

et al. 2020

J Image Video Proc.

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“…In this subsection, a sparsity constraint is imposed on the hidden layer. Sparsity is a recently proposed technique to improve the generalization of the model [33]. A sparsity regularization term is added to (4), and the new objective functions are given as follows:…”

Section: Classification Rate With Model Sparsitymentioning

confidence: 99%

Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification

et al. 2018

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This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis. In this work, we propose a novel model for lung nodule image feature representation that incorporates both local and global characters. First, lung nodule images are divided into local patches with Superpixel. Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE). The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image. Finally, softmax algorithm is employed for lung nodule type classification, which can assemble the whole training process as an end-to-end mode. Comprehensive evaluations are conducted on the widely used public available ELCAP lung image database. Experimental results with regard to different parameter setting, data augmentation, model sparsity, classifier algorithms, and model ensemble validate the effectiveness of our proposed approach.

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“…The noise and holes affect the accuracy of 3D object reconstruction, therefore, the denoising and hole-filling algorithms are used for 3D reconstruction systems [8,9,7,10,11]. Traditional 3D depth denoising methods are focused on fusing multiple consecutive noisy depths to get a higher quality: a method based on the correlation between aligned color and depth frames provided by such sensors [12,13]; spatial-temporal denoising approaches [14,15]; a deep-learning based approach which makes use of aligned gray images to denoise depth data [16]. Enhancing the quality of the depth map obtained with a single depth frame is an increasingly popular research task: wavelet denoising [17]; total variation regularization [18]; median filtering based on adaptive weighted Gaussian [19]; bilateral filter [20]; non-Local-Mean method [21].…”

Section: Introductionmentioning

confidence: 99%

A novel switching bilateral filtering algorithm for depth map

2019

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In this paper, we propose a novel switching bilateral filter for depth map from a RGB-D sensor. The switching method works as follows: the bilateral filter is applied not at all pixels of the depth map, but only in those where noise and holes are possible, that is, at the boundaries and sharp changes. With the help of computer simulation we show that the proposed algorithm can effectively and fast process a depth map. The presented results show an improvement in the accuracy of 3D object reconstruction using the proposed depth filtering. The performance of the proposed algorithm is compared in terms of the accuracy of 3D object reconstruction and speed with that of common successful depth filtering algorithms.

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Fast depth image denoising and enhancement using a deep convolutional network

Cited by 52 publications

References 9 publications

Defeating data hiding in social networks using generative adversarial network

Defeating data hiding in social networks using generative adversarial network

Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification

A novel switching bilateral filtering algorithm for depth map

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