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FREQUENCY-DOMAIN ANALYSIS BASED EXPLOITATION OF COLOR CHANNELS FOR COLOR image DEMOSAICKING

FREQUENCY-DOMAIN ANALYSIS BASED EXPLOITATION OF COLOR CHANNE...

引用

IEEE visual communications and image processing conference

作者： Sunil Prasad Jaiswal Vinit Jakhetiya Ke Gu Sharath C. Guntuku Ashutosh Singla Beijing University of Technology China IIT Jammu India University of Pennsylvania USA TU Ilmenau Germany

Color-difference interpolation (CDI) has been a widely used technique for various color demosaicking methods. CDI-based methods perform interpolation in the color-difference domain assuming that the color-difference signal is a low-pass signal. Recently, a residual interpolation (RI) algorithm, which conducts interpolation in the residual domain, has been developed, and it assumes that the residual domain is flatter or smoother than the channel-difference domain. In this paper, we comprehensively show a frequency domain analysis of these assumptions and observe that it is image dependent and creates artifacts in the interpolated image. With this view, we propose an algorithm that uses the inter-color correlation as well as the residual smoothness among the different channel much better than the existing algorithms. Experimental results emphasize that the proposed algorithm atribute better performances the existing algorithms in terms of both visual and objective quality.

关键词： image color analysis Prediction algorithms Frequency-domain analysis Interpolation image reconstruction Correlation Approximation algorithms

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Learn image Object Co-segmentation with Multi-scale Feature Fusion

Learn Image Object Co-segmentation with Multi-scale Feature ...

引用

IEEE visual communications and image processing conference

作者： Lina Li Zhi Liu Jian Zhang Xiaofei Zhou Shanghai University Shanghai China University of Technology Sydney Sydney Australia

image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-the-art co-segmentation methods while the computation cost has been effectively reduced.

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image Segmentation Based Graph-Cut Approach to Fast Color image Coding via Graph Fourier Transform

Image Segmentation Based Graph-Cut Approach to Fast Color Im...

引用

IEEE visual communications and image processing conference

作者： Kaito Abiko Takayuki Hamamoto Kazuniri Uruma Mamoru Sugawara Seiichiro Hangai Tokyo University of science Tokyo Japan Kogakuin University Tokyo Japan

Colorization-based image coding is a technique to compress chrominance information of an image using a colorization technique. The conventional algorithm applies graph Fourier transform to the colorization-based coding. In this algorithm, several pixels on the image are defined as vertices of the graph, and the chrominance values of that pixels are set as graph signals. Then, the graph signal corresponding to the several chrominance values on the image is transformed to the graph spectrum based on the graph Fourier transform, and the graph spectrum is compressed and stored. Because the stored graph spectrum gives the graph signal on the image based on the inverse graph Fourier transform in decoding phase, the color image is recovered from the luminance image and the several chrominance values corresponding to the graph signal. However, high calculation time is required to perform graph Fourier transform, and therefore, this paper proposes a fast graph Fourier transform to improve the conventional colorization-based image coding algorithm. In numerical examples, although the PSNR value is decreased 0.3 dB, the proposed algorithm is 16.8 times faster than the conventional method.

关键词： image coding Fourier transforms Signal processing algorithms Color image segmentation Decoding Laplace equations

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No-Reference Stereoscopic image Quality Assessment Based On Shuffle-Convolutional Neural Network

No-Reference Stereoscopic Image Quality Assessment Based On ...

引用

IEEE visual communications and image processing conference

作者： Sumei Li Mingyi Wang Chunping Hou School of Electrical and Information Engineering Tianjin University Tianjin China

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has been gaining great attention. In this work, to find a better SIQA method conforming to the perceptual characteristics of our brain, we propose a two-channel convolutional neural network (CNN) based on shuffle unit, which is called SCNN, for no-reference SIQA. The shuffle unit is used to mix up the features extracted from the left and right views to complete information communication between the two views. Different from other SIQA methods, the four shuffle units among proposed model achieve the multiple binocular fusions while processing the left and right views. Moreover, the Shuffle v2 block before the global pooling layer further improves the accuracy of SCNN. In addition, it is worth noting that we employ decorrelated batch normalization (DBN) to obtain the better generalization ability. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods.

关键词： Feature extraction Stereo image processing Optical imaging Optical fibers Optical network units visualization

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Asymmetric Supervised Deep Autoencoder for Depth image based 3D Model Retrieval

Asymmetric Supervised Deep Autoencoder for Depth Image based...

引用

IEEE visual communications and image processing conference

作者： Ayesha Siddiqua Guoliang Fan School of Electrical and Computer Engineerin Oklahoma State University Stillwater OK USA

In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images. The asymmetric supervised autoencoder is trained with real and synthetic depth images together. The novelty of this research lies in the asymmetric structure of a supervised deep autoencoder. The proposed asymmetric deep supervised autoencoder deals with the incompleteness and ambiguity present in the depth images by balancing reconstruction and classification capabilities in a unified way with mixed depth images. We investigate the relationship between the encoder layers and decoder layers, and claim that an asymmetric structure of a supervised deep autoencoder reduces the chance of overfitting by 8% and is capable of extracting more robust features with respect to the variance of input than that of a symmetric structure. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross modal 3D model retrieval.

关键词： Asymmetric autoencoder Supervised learning

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image-Based End-to-End Neural Network for Dense Disparity Estimation

Image-Based End-to-End Neural Network for Dense Disparity Es...

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IEEE visual communications and image processing conference

作者： Shuqiao Sun Rongke Liu Qiuchen Du Shantong Sun Shaoli Kang School of Electronic and Information Beihang University Beijing China

Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel image-based convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hour-glass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in several datasets.

关键词： Feature extraction Estimation Training Benchmark testing image resolution Computer architecture Task analysis

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An Improved QoE Evaluation Model for HTTP Adaptive Streaming

An Improved QoE Evaluation Model for HTTP Adaptive Streaming

引用

IEEE visual communications and image processing conference

作者： Zaixin Yang Tiantian He Li Song Rong Xie Xiao Gu Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

HTTP adaptive streaming (HAS) is an adaptive bitrate streaming technique that enables high quality streaming of media content over the Internet delivered from conventional HTTP web servers. ITU-T Rec. P.1203.3 is the first standardized Quality of Experience model for audiovisual HTTP Adaptive Streaming. It takes into account the subjective impact of HAS-typical effects (such as buffering, quality switches) on users. But the buffering inputs required for the model is too complex and redundant, and it does not consider the impact of the worst video quality on QoE. In the paper we optimize the ITU-T Rec. P.1203.3 model in the above two aspects, simplify model input and improve model accuracy and stability.

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Multi-view high dynamic range reconstruction via gain estimation

Multi-view high dynamic range reconstruction via gain estima...

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IEEE visual communications and image processing conference

作者： Firas Abedi Qiong Liu You Yang Computer Technical Engineering Department The Islamic University Najaf Iraq School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

Multi-view high dynamic range reconstruction is a challenging problem, especially if the multi-view low dynamic range images are obtained from cameras arranged sparsely with limited shared view of vision among them. In this paper, we address the above challenge in addition to the back-lighting problem. We first enclose the geometry characteristic of the scene to rectify the outlier feature points. Consequently, an exposure gain is calculated according to those rectified features. After that, we extend the dynamic range for the multi-view low dynamic range images based on the estimated gain, then, generate a final high dynamic range image per view. Experimental results demonstrate superior performance for the proposed method over state-of-the-art methods in both objective and subject comparisons. These results suggest that our method is suitable to improve the visual quality of multi-view low dynamic range images captured in low back-lighting conditions via commercial cameras sparsely located among each other.

关键词： Cameras image restoration image reconstruction Dynamic range Brightness Standards

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Morphology Reconstruction of Obstructed Coronary Artery in Angiographic images

Morphology Reconstruction of Obstructed Coronary Artery in A...

引用

IEEE visual communications and image processing conference

作者： Tianming Du Xiaotong Shi Ruijia Wu Honggang Zhang Xiaolin Song Jin Feng Fuzhuo Sun Beijing University of Posts and Telecommunications Beijing China Beijing Normal University Beijing China

In cardiac arterial interventional therapy, coronary angiograms provides key information to physicians for treatment strategy selection. However, it is hard to extract the information about arteries with CTO lesion in absence of contrast opacification and visualization of the CTO coronary arteries in angiograms. In this paper, we present an algorithm, which can predict the extension direction of artery with CTO and reconstruct the morphology of blocked artery in coronary angiograms. First, our algorithm segment the CTO artery angiogram and extract the artery skeleton. Second, an iterative approach is preformed to reconstruct the skeleton of blocked artery. Finally, our algorithm generates the simulated postoperative angiogram according to the skeleton image. The results demonstrate that automatic morphology reconstructing of CTO coronary artery with high accuracy and reliability is feasible.

关键词： Arteries Skeleton Morphology image segmentation image reconstruction Prediction algorithms Task analysis

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Pyramid Real image Denoising Network

Pyramid Real Image Denoising Network

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IEEE visual communications and image processing conference

作者： Yiyun Zhao Zhuqing Jiang Aidong Men Guodong Ju Beijing University of Posts and Telecommunications Beijing China GuangDong TUS-TuWei Technology Co. Ltd Guangzhou China

While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-the-art denoisers in terms of both quantitative measure and visual perception quality.

关键词： Noise reduction Kernel Noise measurement Feature extraction image denoising Estimation visualization

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