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A multi-stage block matching motion estimation for superresolution video reconstruction

A multi-stage block matching motion estimation for superreso...

引用

conference on visual communications and image processing 2002

作者： Hong, L Sannino, R Kari, J STMicroelect AST San Diego Lab San Diego CA 92121 USA

ISBN: (纸本)0819444111

The performance of superresolution video enhancement relies heavily on the robustness and accuracy of motion estimation techniques. In this paper, we propose a novel and efficient block matching motion estimation algorithm suitable for estimating general motion existing in low resolution video frames. We exploit the spatial correlations between motion vectors and apply a coarse-to-fine multi-stage scheme to get the dense motion fields. We incorporate our motion estimation technique into the Projection Onto Convex Sets (POCS) superresolution framework. Experimental results show that the resulting high resolution images yield visual sharper enhanced images with significant PSNR improvement.

关键词： image and video processing motion estimation resolution enhancement superresolution

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3D Tensor Display for Non-Lambertian Content

3D Tensor Display for Non-Lambertian Content

引用

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

作者： Losfeld, Armand Soetens, Eline Bonatto, Daniele Fachada, Sarah Van Bogaert, Laurie Lafruit, Gauthier Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium

ISBN: (纸本)9781665475921

A tensor display is a type of 3D light field display, composed of multiple transparent screens and a back-light that can render a scene with correct depth, allowing to view a 3D scene without wearing glasses. The analysis of state-of-the-art tensor displays assumes that the content is Lambertian. In order to extend its capabilities, we analyze the limitations of displaying non-Lambertian scenes and propose a new method to factorize the non-Lambertian scenes using disparity analysis. Moreover, we demonstrate a new prototype of a tensor display with three layers of full HD content at 60 fps. Compared with state-of-the-art, the evaluation results verify that the proposed non-Lambertian rendering method can display a higher quality for non-Lambertian scenes on both simulation and a prototyped tensor display.

关键词： Non-Lambertian prototype tensor display depth range disparity range simulation light field

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iWave3D: End-to-end Brain image Compression with Trainable 3-D Wavelet Transform

iWave3D: End-to-end Brain Image Compression with Trainable 3...

引用

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

作者： Xue, Dongmei Ma, Haichuan Li, Li Liu, Dong Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781728185514

With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods. At present, the most commonly used compression methods are all based on 3-D wavelet transform, such as JP3D. However, traditional 3-D wavelet transforms are designed manually with certain assumptions on the signal, but brain images are not as ideal as assumed. What's more, they are not directly optimized for compression task. In order to solve these problems, we propose a trainable 3-D wavelet transform based on the lifting scheme, in which the predict and update steps are replaced by 3-D convolutional neural networks. Then the proposed transform is embedded into an end-to-end compression scheme called iWave3D, which is trained with a large amount of brain images to directly minimize the rate-distortion loss. Experimental results demonstrate that our method outperforms JP3D significantly by 2.012 dB in terms of average BD-PSNR.

关键词： Brain image compression wavelet transform lifting scheme 3-D convolutional neural networks

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Drone-Based Car Counting via Density Map Learning

Drone-Based Car Counting via Density Map Learning

引用

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

作者： Huang, Jingxian Ding, Guanchen Guo, Yujia Yang, Daiqin Wang, Sihan Wang, Tao Zhang, Yunfei Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Inc Shenzhen 518052 Peoples R China

ISBN: (纸本)9781728180687

Car counting on drone-based images is a challenging task in computer vision. Most advanced methods for counting are based on density maps. Usually, density maps are first generated by convolving ground truth point maps with a Gaussian kernel for later model learning (generation). Then, the counting network learns to predict density maps from input images (estimation). Most studies focus on the estimation problem while overlooking the generation problem. In this paper, a training framework is proposed to generate density maps by learning and train generation and estimation subnetworks jointly. Experiments demonstrate that our method outperforms other density map-based methods and shows the best performance on drone-based car counting.

关键词： Car counting density map learning drone-based image

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A novel algorithm for detection of rapid approaching objects

A novel algorithm for detection of rapid approaching objects

引用

IEEE International conference on Acoustics, Speech, and Signal processing

作者： Roewer, F Kleine, U Univ Magdeburg D-39106 Magdeburg Germany

ISBN: (纸本)0780374029

In this article a novel optimized image processing algorithm for replication of fast motion perception of the human visual system [1], [2] is presented. Rapid approaching objects are detected using a CMOS pixel array with internal parallel image processing. The new algorithm will be discussed with several examples. The response time is about 10mus.

关键词： object detection image motion analysis visual perception CMOS image sensors array signal processing parallel processing feature extraction object detection image processing algorithm human visual perception fast motion perception CMOS pixel array parallel processing parameter extraction 10 to 20 mus

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FRIDAY: Mitigating Unintentional Facial Identity in Deepfake Detectors Guided by Facial Recognizers

FRIDAY: Mitigating Unintentional Facial Identity in Deepfake...

引用

2024 conference on visual communications and image processing

作者： Kim, Younghun Kwon, Myung-Joon Lee, Wonjun Kim, Changick Korea Adv Inst Sci & Technol Grad Sch Green Growth & Sustainabil Daejeon South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (纸本)9798331529543;9798331529550

Previous Deepfake detection methods perform well within their training domains, but their effectiveness diminishes significantly with new synthesis techniques. Recent studies have revealed that detection models make decision boundaries based on facial identity instead of synthetic artifacts, leading to poor cross-domain performance. To address this issue, we propose FRIDAY, a novel training method that attenuates facial identity utilizing a face recognizer. To be specific, we first train a face recognizer using the same backbone as the Deepfake detector. We then freeze the recognizer and use it during the detector's training to mitigate facial identity information. This is achieved by feeding input images into both the recognizer and the detector, then minimizing the similarity of their feature embeddings using our Facial Identity Attenuating loss. This process encourages the detector to produce embeddings distinct from the recognizer, effectively attenuating facial identity. Comprehensive experiments demonstrate that our approach significantly improves detection performance on both in-domain and cross-domain datasets.

关键词： Deepfake Detection Unintentional Facial Identity Face Recognition image Forensics

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A review of data preprocessing modules in digital image forensics methods using deep learning

A review of data preprocessing modules in digital image fore...

引用

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

作者： Berthet, Alexandre Dugelay, Jean-Luc Eurecom Dept Digital Secur Biot France

ISBN: (纸本)9781728180687

Access to technologies like mobile phones contributes to the significant increase in the volume of digital visual data (images and videos). In addition, photo editing software is becoming increasingly powerful and easy to use. In some cases, these tools can be utilized to produce forgeries with the objective to change the semantic meaning of a photo or a video (e.g. fake news). Digital image forensics (DIF) includes two main objectives: the detection (and localization) of forgery and the identification of the origin of the acquisition (i.e. sensor identification). Since 2005, many classical methods for DIF have been designed, implemented and tested on several databases. Meantime, innovative approaches based on deep learning have emerged in other fields a nd have surpassed traditional techniques. In the context of DIF, deep learning methods mainly use convolutional neural networks (CNN) associated with significant preprocessing modules. This is an active domain and two possible ways to operate preprocessing have been studied: prior to the network or incorporated into it. None of the various studies on the digital image forensics provide a comprehensive overview of the preprocessing techniques used with deep learning methods. Therefore, the core objective of this article is to review the preprocessing modules associated with CNN models.

关键词： Semantics

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Single image Super-Resolution Using ConvNeXt

Single Image Super-Resolution Using ConvNeXt

引用

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

作者： You, Chenghui Hong, Chaoqun Liu, Lijuan Lin, Xuehan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen Peoples R China

ISBN: (纸本)9781665475921

In recent years, a lot of deep convolution neural networks have been successfully applied in single image super-resolution (SISR). Even in the case of using small convolution kernel, those methods still require large number of parameters and computation. To tackle the problem above, we propose a novel framework to extract features more efficiently. Inspired by the idea of deep separable convolution, we improve the standard residual block and propose the inverted bottleneck block (IBNB). The IBNB replaces the small-sized convolution kernel with the large-sized convolution kernel without introducing additional computation. The proposed IBNB proves that large kernel size convolution is available for SISR. Comprehensive experiments demonstrate that our method surpasses most methods by up to 0.10 similar to 0.32dB in quantitative metrics with fewer parameters.

关键词： single image super-resolution convolutional neural network deep separable convolution

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Variable Bitrate Models For Learned image Compression with Multi-gain units and Weighted Probability Assignment

Variable Bitrate Models For Learned Image Compression with M...

引用

2024 conference on visual communications and image processing

作者： Wang, Ran Jiang, Wen Sun, Heming Katto, Jiro Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa Japan Waseda Univ Waseda Res Inst Sci & Engn Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

With the advancement of deep learning techniques, learned image compression (LIC) has surpassed traditional compression methods. However, these methods typically require training separate models to achieve optimal rate-distortion performance, leading to increased time and resource consumption. To tackle this challenge, we propose leveraging multi-gain and inverse multi-gain unit pairs to enable variable rate adaptation within a single model. Nevertheless, experiments have shown that rate-distortion performance may degrade at certain bitrates. Therefore, we introduce weighted probability assignment, where different selection probabilities are assigned during training based on lambda values, to increase the model's training frequency under specific bitrate conditions. To validate our approach, extensive experiments were conducted on Transformer-based and CNN-based models. The experimental results validate the efficiency of our proposed method.

关键词： Learned image compression variable rate multigain units weighted probability assignment

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Algorithm for viewpoint estimation and registration

Algorithm for viewpoint estimation and registration

引用

conference on visual communications and image processing 2002

作者： Calcopietro, F Carli, M Lucchese, L Neri, A Ericsson Enterprise (Italy) Univ. degli Studi di Roma Tre (United States) Univ. of California/Santa Barbara (United States) Univ. degli Studi di Roma Tre (Italy)

ISBN: (纸本)0819444111

This paper presents a simple technique for estimating the space location from which a certain image has been taken. The basic assumption is that the scene portrayed in the image is planar. The method is based on the acquisition of a new set of images, closely resembling the given image. The location is recovered from the parameters describing the camera's pose during the acquisition of that among the new images showing the highest degree of correlation with the original image. An example of application of this technique is discussed in the paper.

关键词： image registration viewpoint estimation

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