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共 4888 条记录，以下是71-80 订阅

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Underwater image Enhancement with Multi-Scale Residual Attention Network

Underwater Image Enhancement with Multi-Scale Residual Atten...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Ueki, Yosuke Ikehara, Masaaki Keio Univ EEE Dept Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781728185514

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.

关键词： image processing image enhancement image restoration underwater image attention

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No-Reference Stereoscopic image Quality Assessment Considering Binocular Disparity and Fusion Compensation

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Feng Jinhui Li, Sumei Chang, Yongli Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.

关键词： stereoscopic image quality assessment dual stream ocular dominance binocular disparity binocular fusion

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No-Reference Stereoscopic image Quality Assessment Based on Convolutional Neural Network with A Long-Term Feature Fusion

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

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Sumei Wang, Mingyi Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

With the rapid development of three-dimensional (3D) technology, the effective stereoscopic image quality assessment (SIQA) methods are in great demand. Stereoscopic image contains depth information, making it much more challenging in exploring a reliable SIQA model that fits human visual system. In this paper, a no-reference SIQA method is proposed, which better simulates binocular fusion and binocular rivalry. The proposed method applies convolutional neural network to build a dual-channel model and achieve a long-term process of feature extraction, fusion, and processing. What's more, both high and low frequency information are used effectively. Experimental results demonstrate that the proposed model outperforms the state-of-the-art no-reference SIQA methods and has a promising generalization ability.

关键词： stereoscopic image quality assessment (SIQA) convolution neural network (CNN) human visual system (HVS) binocular fusion

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Quality Assessment of Screen Content images Based on Multi-Pathway Convolutional Neural Network

Quality Assessment of Screen Content Images Based on Multi-P...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Mengyao Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665475921

In this paper, considering the retinal structure of human eye, and the composition characteristics of screen content images (SCIs), a multi-pathway convolutional neural network (CNN) with picture-text competition is proposed for SCIs quality assessment. According to the visual mechanism of human retina, we design a retinal structure simulation module, which uses multiple parallel convolution pathways to simulate the parallel transmission of visual signals by bipolar cells and uses a multi-pathway feature fusion (MPFF) module to allocate the weight for each channel to simulate horizontal cells' regulation of the information transmission. In addition, we design an adaptive feature extraction and competition module (AFEC) to directly extract the features of textural and pictorial regions and distribute the weight. Furthermore, the attention module combined with deformable convolution and channel attention can accurately extract image edge features and reduce redundancy of information. Experimental results show that the proposed method is superior to the mainstream methods.

关键词： image quality assessment screen content image convolutional neural network

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Improving the Perceived Quality of Half Tone Secret images in visual Cryptography 23

Improving the Perceived Quality of Half Tone Secret Images i...

引用

23nd Signal processing and communications Applications conference (SIU)

作者： Tetik, Yusuf Engin Yildizhan, Aytekin Erol, Kadir Kara Harp Okulu Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781467373869

In this work, a method that aims to improve the half tone images hidden by visual cryptography is proposed. visual cryptography produces shared images each of which does not have any hint about the hidden image. When these shared images stacked over one another, the hidden image is revealed without any need of post processing or decoding. The operations that make it impossible to guess the hidden image from a single shared one, also causes the deterioration of the hidden image to a degree. In this work, it is aimed to process the hidden image in a way to reduce the disruption caused by the operations required by visual cryptography. A new method for producing half tone images from gray tone images that is suitable for our aim is introduced and it is shown that how this method produces images that have higher perceptual quality after visual cryptography is applied.

关键词： visual Cryptography Half Tone images Cryptography image processing

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NIR image colorization with graph-convolutional neural networks

NIR image colorization with graph-convolutional neural netwo...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Valsesia, Diego Fracastoro, Giulia Magli, Enrico Politecn Torino Turin Italy

ISBN: (纸本)9781728180687

Colorization of near-infrared (NIR) images is a challenging problem due to the different material properties at the infared wavelenghts, thus reducing the correlation with visible images. In this paper, we study how graph-convolutional neural networks allow exploiting a more powerful inductive bias than standard CNNs, in the form of non-local self-similiarity. Its impact is evaluated by showing how training with mean squared error only as loss leads to poor results with a standard CNN, while the graph-convolutional network produces significantly sharper and more realistic colorizations.

关键词： NIR colorization graph neural network

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Deformable Convolution Based No-Reference Stereoscopic image Quality Assessment Considering visual Feedback Mechanism

Deformable Convolution Based No-Reference Stereoscopic Image...

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Zhou, Mingyue Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

Simulation of human visual system (HVS) is very crucial for fitting human perception and improving assessment performance in stereoscopic image quality assessment (SIQA). In this paper, a no-reference SIQA method considering feedback mechanism and orientation selectivity of HVS is proposed. In HVS, feedback connections are indispensable during the process of human perception, which has not been studied in the existing SIQA models. Therefore, we design a new feedback module (FBM) to realize the guidance of the high-level region of visual cortex to the low-level region. In addition, given the orientation selectivity of primary visual cortex cells, a deformable feature extraction block is explored to simulate it, and the block can adaptively select the regions of interest. Meanwhile, retinal ganglion cells (RGCs) with different receptive fields have different sensitivities to objects of different sizes in the image. So a new multi receptive fields information extraction and fusion manner is realized in the network structure. Experimental results show that the proposed model is superior to the state-of-the-art no-reference SIQA methods and has excellent generalization ability.

关键词： stereoscopic image quality assessment (SIQA) human visual system (HVS) feedback mechanism deformable convolution

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Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for image Denoising

Deep Convolutional Neural Network Based on Multi-Scale Featu...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhang, Jing Sang, Liu Wan, Zekang Wang, Yuchen Li, Yunsong Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China

ISBN: (纸本)9781728180687

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.

关键词： image denoising multi-scale convolutional network diamond

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Towards Universal GAN image Detection

Towards Universal GAN Image Detection

引用

IEEE International conference on visual communications and image processing (VCIP) - visual communications in the Era of AI and Limited Resources

作者： Cozzolino, Davide Gragnaniello, Diego Poggi, Giovanni Verdoliva, Luisa Univ Federico II Naples Naples Italy

ISBN: (纸本)9781728185514

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.

关键词： Fake detection

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Deviation Control for Learned image Compression

Deviation Control for Learned Image Compression

引用

2024 conference on visual communications and image processing

作者： Li, Yuqi Zhang, Haotian Song, Xiaomin Liu, Zheng Zheng, Huiming Li, Li Liu, Dong Univ Sci & Technol China Beijing Peoples R China Sichuan Xinshi Chuangwei Ultra HD Technol Co Ltd Sichuan Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Most approaches in learned image compression follow the transform coding scheme. The characteristics of latent variables transformed from images significantly influence the performance of codecs. In this paper, we present visual analyses on latent features of learned image compression and find that the latent variables are spread over a wide range, which may lead to complex entropy coding processes. To address this, we introduce a Deviation Control (DC) method, which applies a constraint loss on latent features and entropy parameter mu. Training with DC loss, we obtain latent features with smaller values of coding symbols and s, effectively reducing entropy coding complexity. Our experimental results show that the plug-and-play DC loss reduces entropy coding time by 30-40% and improves compression performance.

关键词： Deviation Control Latent Features Learned image Compression

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