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检索条件"任意字段=2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014"

共 2409 条记录，以下是21-30 订阅

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visual Analysis Motivated Super-Resolution Model for image Reconstruction

Visual Analysis Motivated Super-Resolution Model for Image R...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Wang, Huifen Xue, Junda Yang, Mingchuan Zhang, Yuan China Telecom Res Inst Coll Big Data & Artificial Intelligence Shanghai Peoples R China China Telecom Res Inst Coll Big Data & Artificial Intelligence Beijing Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China China Telecom Res Inst Coll Big Data & Artificial Intelligence Hangzhou Peoples R China

ISBN: (纸本)9781665475921

This paper presents a concise end-to-end visual analysis motivated super-resolution model VASR for image reconstruction. Compatible with the existing machine vision feature coding framework, the features extracted from the machine vision task model are super-resolution amplified to reconstruct the original image for human vision. The experimental results show that without additional bit-streams, VASR can well complete the task of image reconstruction based on the extracted machine features, and has achieved good results on COCO, Openimages, TVD, and DIV2K datasets.

关键词： coding for machines image reconstruction super-resolution

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Geometric-visual descriptor for improved image based localization

Geometric-visual descriptor for improved image based localiz...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Ouni, Achref Royer, Eric Chevaldonne, Marc Dhome, Michel Univ Clermont Auvergne Inst Pascal Clermont Ferrand France

ISBN: (纸本)9781728180687

This paper addresses the problem of image based localization. The goal is to find quickly and accurately the relative pose from a query taken from a stereo camera and a map obtained using visual SLAM which contains poses and 3D points associated to descriptors. In this paper we introduce a new method that leverages the stereo vision by adding geometric information to visual descriptors. This method can be used when the vertical direction of the camera is known (for example on a wheeled robot). This new geometric visual descriptor can be used with several image based localization algorithms based on visual words. We test the approach with different datasets (indoor, outdoor) and we show experimentally that the new geometricvisual descriptor improves standard image based localization approaches.

关键词： Descriptors Retrieval images Pose estimation Localization

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Learning From Paired and Unpaired Data: Alternately Trained CycleGAN for Near Infrared image Colorization

Learning From Paired and Unpaired Data: Alternately Trained ...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Yang, Zaifeng Chen, Zhenghua ASTAR Inst High Performance Comp Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781728180687

This paper presents a novel near infrared (NIR) image colorization approach for the Grand Challenge held by 2020 ieee International conference on visual communications and image processing (vcip). A Cycle-Consistent Generative Adversarial Network (CycleGAN) with cross-scale dense connections is developed to learn the color translation from the NIR domain to the RGB domain based on both paired and unpaired data. Due to the limited number of paired NIR-RGB images, data augmentation via cropping, scaling, contrast and mirroring operations have been adopted to increase the variations of the NIR domain. An alternating training strategy has been designed, such that CycleGAN can efficiently and alternately learn the explicit pixel-level mappings from the paired NIR-RGB data, as well as the implicit domain mappings from the unpaired ones. Based on the validation data, we have evaluated our method and compared it with conventional CycleGAN method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and angular error (AE). The experimental results validate the proposed colorization framework.

关键词： vcip challenge 2020 NIR image colorization CycleGAN UNet paired/unpaired images data augmentation

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UGNet: Underexposed images Enhancement Network based on Global Illumination Estimation

UGNet: Underexposed Images Enhancement Network based on Glob...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Fang, Yuan Zhu, Wenzhe Zhu, Qing Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781728180687

This paper proposes a new neural network for enhancing underexposed images. Instead of the decomposition method based on Retinex theory, we introduce smooth dilated convolution to estimate global illumination of the input image, and implement an end-to-end learning network model. Based on this model, we formulate a multi-term loss function that combines content, color, texture and smoothness losses. Our extensive experiments demonstrate that this method is superior to other methods in underexposed image enhancement. It can cover more color details and be applied to various underexposed images robustly.

关键词： underexposed enhancement global illumination estimation image restoration smooth dilated 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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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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Alpha-trimmed Mean Filter and XOR based image Enhancement for Embedding Data in image

Alpha-trimmed Mean Filter and XOR based Image Enhancement fo...

引用

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

作者： Alam, S. M. Ammar Ul Huda, Moid Farhan, Muhammad Habib Univ Dhanani Sch Sci & Engr Karachi Pakistan

ISBN: (纸本)9781728185514

In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.

关键词： steganography alpha-trimmed mean LSB manipulation image enhancement data embedding

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A Deep Learning-based Audio-in-image Watermarking Scheme

A Deep Learning-based Audio-in-Image Watermarking Scheme

引用

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

作者： Das, Arjon Zhong, Xin Univ Nebraska Omaha Dept Comp Sci Omaha NE 68182 USA

ISBN: (纸本)9781728185514

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.

关键词： Audio-in-image watermarking deep learning neural networks robustness

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Stereoscopic image reflection removal based on Wasserstein Generative Adversarial Network

Stereoscopic image reflection removal based on Wasserstein G...

引用

ieee International conference on visual communications and image processing (vcip)

作者： Wang, Xiuyuan Pan, Yikun Lun, Daniel P. K. Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

ISBN: (纸本)9781728180687

Reflection removal is a long-standing problem in computer vision. In this paper, we consider the reflection removal problem for stereoscopic images. By exploiting the depth information of stereoscopic images, a new background edge estimation algorithm based on the Wasserstein Generative Adversarial Network (WGAN) is proposed to distinguish the edges of the background image from the reflection. The background edges are then used to reconstruct the background image. We compare the proposed approach with the state-of-the-art reflection removal methods. Results show that the proposed approach can outperform the traditional single-image based methods and is comparable to the multiple-image based approach while having a much simpler imaging hardware requirement.

关键词： Reflection removal GAN stereoscopic images

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A Lightweight Tire Tread image Classification Network

A Lightweight Tire Tread Image Classification Network

引用

ieee International conference on visual communications and image processing (vcip)

作者： Zhang, Fenglei Li, Da Li, Shenghua Guan, Weili Liu, Meng Shandong Jianzhu Univ Jinan Peoples R China Monash Univ Elbourne Australia

ISBN: (纸本)9781665475921

vcip 2022 "Tire pattern image classification based on lightweight network challenge" aims to design lightweight networks that correctly classify tire surface tread patterns and indentation images using less overhead. To this end, we present a novel lightweight tire tread classification network. Concretely, we adopt the ShuffleNet-V2-x0.5 network as our backbone. To reduce the computation complexity, we introduce the Space-To-Depth and Anti-Alias Downsampling modules to pre-process the input image. Moreover, to enhance the classification ability of our model, we adopt the knowledge distillation strategy by considering Vision Transformer as the teacher network. To ensure the robustness of our model, we pre-train it on imageNet and fine-tune the training set of the challenge. Experiments on the challenge dataset demonstrate that our model achieves superior performance, with 99.00% classification accuracy, 25.51M FLOPs, and 0.20M parameters.

关键词： tire tread image classification knowledge distillation lightweight image classification

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