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

共 25554 条记录，以下是301-310 订阅

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LOFT: LATENT SPACE OPTIMIZATION AND GENERATOR FINE-TUNING FOR DEFENDING AGAINST DEEPFAKES 49

LOFT: LATENT SPACE OPTIMIZATION AND GENERATOR FINE-TUNING FO...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zeng, Shaoyou Wang, Wenhao Huang, Fangjun Fang, Yanmei Sun Yat Sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

DeepFakes pose a significant threat to individual reputations and society as a whole. Existing proactive defense strategies concentrate on adding adversarial perturbations to images to disrupt or nullify the generation of DeepFakes, but these approaches are easily detectable by human perception and can be removed. To address this challenge, we propose a three-stage framework called LOFT (Latent Space Optimization and Generator Fine-Tuning for Defending against DeepFakes). First, encoding the original image into the latent space to obtain a latent code that captures facial features. Second, utilizing Adversarial Latent Optimization to optimize the latent code for reconstructing the image and defending against DeepFake manipulation. Third, fine-tuning the generator to enhance the reconstructed image's visual quality and defense capability further. Our study evaluates the effectiveness of our proposed framework through two distinct DeepFake tasks: attribute editing and face reenactment. Various experimental results demonstrate that our proposed framework outperforms the existing benchmark in both visual quality and defense capability.

关键词： DeepFake proactive defense adversarial attack latent representations image forensics

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TOWARD SUFFICIENT SPATIAL-FREQUENCY INTERACTION FOR GRADIENT-AWARE UNDERWATER image ENHANCEMENT 49

TOWARD SUFFICIENT SPATIAL-FREQUENCY INTERACTION FOR GRADIENT...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhao, Chen Cai, Weiling Dong, Chenyu Zeng, Ziqi Nanjing Normal Univ Sch Artificial Intelligence Nanjing 210023 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement. The code is available at https://***/zhihefang/SFGNet.

关键词： Underwater image enhancement gradient map spatial-frequency interaction

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PERCEPTION-ORIENTED OMNIDIRECTIONAL image SUPER-RESOLUTION BASED ON TRANSFORMER NETWORK 30

PERCEPTION-ORIENTED OMNIDIRECTIONAL IMAGE SUPER-RESOLUTION B...

引用

30th ieee International conference on image processing (ICIP)

作者： An, Hongyu Zhang, Xinfeng Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728198354

Omnidirectional image (ODI) super-resolution (SR) is an important technique in augmented reality and virtual reality applications to address the low-resolution problem caused by limitations in capturing devices or bandwidth. The ODI projection distortion makes it challenging to apply existing SR methods. In this paper, we propose an ODI SR method by leveraging the characteristics of ODIs and human visual characteristics. Specifically, we firstly design a perception-orientated adaptive loss function by jointly utilizing saliency map and latitude map. In our proposed ODI-SR network, we introduce an attention module to aggregate multi-scale information and leverage spherical convolution to adapt to the spheric format of ODIs. Furthermore, we design a data augmentation strategy for ODIs according to viewpoint distribution to further improve the visual quality of SR images. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art performance according to both qualitative and quantitative evaluations.

关键词： Omnidirectional image super-resolution perceptual quality

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Hybrid Feature Approach for Enhancing Zero-Shot image Classification 4th

Hybrid Feature Approach for Enhancing Zero-Shot Image Classi...

引用

4th International conference on Artificial Intelligence and Knowledge processing

作者： Khanam, Shaista Sonar, Poonam N. Mumbai Univ Mumbai Maharashtra India

ISBN: (纸本)9783031734762;9783031734779

Zero-shot learning (ZSL) directs the challenge of classifying unseen test images without explicit training on those samples. ZSL can identify and classify unlabeled images available in abundance by learning from visual and semantic embedding vectors (feature vectors). Information-enriched visual features extracted from images play a crucial role in ZSL. This paper proposes a hybrid feature approach that integrates low-level (LL), and high-level (HL) features extracted from images. Gray Level Co-occurrence Matrix (GLCM) and Gabor features are employed to obtain LL texture features, while HL features are derived from the ResNet-50 model, renowned for capturing complex hierarchical representations. These hybrid visual features are then mapped with semantic features using linear mapping, where the semantic features are embedding vectors of labels generated by the fastText model. Experiments on the AWA2 and SUN datasets are conducted in a bid to evaluate the proposed approach's effectiveness. The hybrid feature approach has demonstrated enhanced quality in zero-shot image classification, effectively classifying images that the model has not seen during training.

关键词： Hybrid feature Zero-shot image classification GLCM

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Filling the Gap: Enhancing Ultra-Low Light image Brightness Through Multi-Band NIR Estimation

Filling the Gap: Enhancing Ultra-Low Light Image Brightness ...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Park, Jeong-Hyeok Park, Tae-Sung Kim, Jong-Ok Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

We propose a method for capturing high-quality images in low-light environments using multi-band near-infrared (NIR) images, which offer robustness to brightness variations and provide structural information not present in RGB images. As multi-band NIR images are not commonly available in consumer cameras, an RGB-NIR conversion network is employed to estimate them. Training data comes from a new low-light dataset with NIR-RGB image pairs. Unlike single-band approaches, this method utilizes multi-band NIR images with peak wavelengths of 785nm, 850nm, and 940nm, resulting in enhanced quality. Integration of the RGB-NIR conversion network into an existing low-light enhancement (LLIE) network is achieved using the proposed cross-attention transformer. Experimental results validate the effectiveness of multi-band NIR estimation in enhancing low-light intensity. Moreover, the proposed RGB-NIR conversion network and cross-attention module can be applied to any existing deep LLIE networks. © 2023 ieee.

关键词： image enhancement

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Denoising Diffusion Adversarial Models for Unconditional Medical image Generation 31

Denoising Diffusion Adversarial Models for Unconditional Med...

引用

31st ieee conference on Signal processing and communications Applications (SIU)

作者： Dalmaz, Onat Saglam, Baturay Elmas, Gokberk Mirza, Muhammad Cukur, Tolga Bilkent Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkiye Bilkent Univ Ulusal Manyet Rezonans Arastirma Merkezi UMRAM Ankara Turkiye Bilkent Univ Sinirbilim Programi Muhendislik & Fen Bilimleri Enstitusu Ankara Turkiye

ISBN: (纸本)9798350343557

Unconditional medical image synthesis is the task of generating realistic and diverse medical images from random noise without any prior information or constraints. Synthesizing realistic medical images can enrich the quality and diversity of medical imaging datasets, which in turn, enhance the performance and generalization of deep learning models for medical imaging. Prevalent approach for synthesizing medical images involves generative adversarial networks (GAN) or denoising diffusion probabilistic models (DDPM). However, GAN models that implicitly learn the image distribution are prone to limited sample fidelity and diversity. On the other hand, diffusion models suffer from slow sampling speed due to small diffusion steps. In this paper, we propose a novel diffusion-based method for unconditional medical image synthesis, Diff-Med-Synth, that generates realistic and diverse medical images from random noise. Diff-Med-Synth combines the advantages of denoising diffusion probabilistic models and GANs to achieve fast and efficient image sampling. We evaluate our method on two multi-contrast MRI datasets and show that it outperforms state-of-the-art methods in terms of quality, diversity, and fidelity of the synthesized images.

关键词： denoising diffusion models generative modeling medical image synthesis unconditional generation adversarial networks

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Feature Decomposition and Attribute Augmentation for Attribute-based Person Search

Feature Decomposition and Attribute Augmentation for Attribu...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Wang, Xin Liu, Fangfang Guo, Caili Li, Zheng Zhang, Hao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350359855

The attribute-based person search task aims to find matching pedestrian images by text attributes, which is relevant in scenarios where no query image is given. However, the existing methods exhibit inferior performance due to their inadequate representation of local fine-grained features, which hinders their ability to effectively model intra-class variations. In addition, there is a zero-shot retrieval problem due to the large number of unseen categories in the test set, resulting in suboptimal generalization performance. In this paper, we propose a novel Feature Decomposition and Attribute Augmentation (FDAA) framework to solve the above problems. Firstly, by decomposing the global features of the image from different directions, the local features are extracted at multiple granularities, thus effectively improving the discrimination ability of the model. Secondly, an attribute augmentation strategy is proposed that can effectively expand the combination of attributes during training and improve the generalization ability of the model. Extensive experiments on the PETA, Market-1501 Attribute, and PA100K datasets demonstrate the effectiveness of our proposed method, outperforming state-of-the-art methods. © 2023 ieee.

关键词： image enhancement

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HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR image TRANSLATION 31

HISTOHDR-NET: HISTOGRAM EQUALIZATION FOR SINGLE LDR TO HDR I...

引用

2024 International conference on image processing

作者： Barua, Hrishav Bakul Krishnasamy, Ganesh Wong, KokSheik Dhall, Abhinav Stefanov, Kalin Monash Univ Malaysia Sch Informat Technol Subang Jaya Malaysia TCS Res Robot & Autonomous Syst Lab Kolkata India Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Monash Univ Fac Informat Technol Clayton Vic Australia

ISBN: (纸本)9798350349405;9798350349399

High Dynamic Range (HDR) imaging aims to replicate the high visual quality and clarity of real-world scenes. Due to the high costs associated with HDR imaging, the literature offers various data-driven methods for HDR image reconstruction from Low Dynamic Range (LDR) counterparts. A common limitation of these approaches is missing details in regions of the reconstructed HDR images, which are overor under-exposed in the input LDR images. To this end, we propose a simple and effective method, HistoHDR-Net, to recover the fine details (e.g., color, contrast, saturation, and brightness) of HDR images via a fusion-based approach utilizing histogram-equalized LDR images along with self-attention guidance. Our experiments demonstrate the efficacy of the proposed approach over the state-of-art methods.

关键词： Data fusion Self-attention High dynamic range imaging Deep learning Histogram equalization

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Foreground-Background Disentanglement based on image and Feature Co-Learning for 3D-Aware Generative Models

Foreground-Background Disentanglement based on Image and Fea...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Lee, Sanghyuk Kim, Daeha Song, Byung Cheol Inha University Department of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350359855

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance. © 2023 ieee.

关键词： Volume rendering

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Vision Language Models for Oil Palm Fresh Fruit Bunch Ripeness Classification 8

Vision Language Models for Oil Palm Fresh Fruit Bunch Ripene...

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ieee 8th International conference on Signal and image processing Applications (ieee ICSIPA)

作者： Goh, Jin Yu Yunos, Yusri Md Sheikh, Usman Ullah Ali, Mohamed Sultan Mohamed Univ Teknol Malaysia Fac Elect Engn Johor Baharu 81310 Johor Malaysia Qatar Univ Dept Elect Engn Coll Engn Doha Qatar

ISBN: (纸本)9798350352368

The accurate classification of fresh fruit bunch ripeness is crucial for optimizing oil quality and yield in the palm oil industry. Traditional manual inspection methods are labor-intensive, subjective, and prone to errors, motivating the exploration of automated solutions. This paper examined the potential of vision language models, including LLaVA 1.5, YiVL, and PaliGemma, to automate and enhance FFB ripeness assessment. The models were evaluated on their ability to classify ripeness stages and the accuracy of generated descriptive text using metrics like BLEU and ROUGE scores. Yi-VL achieved the highest descriptive accuracy with a ROUGE-L score of 93.14. However, it processes 0.18 samples per second, which is slower than PaliGemma (0.53 samples/second). PaliGemma is 194.44% more efficient in samples/second than Yi-VL, making it better suited for realtime applications despite its lower accuracy (ROUGE-L: 26.15). LLaVA 1.5 offers a balance between accuracy (ROUGE-L: 82.16) and efficiency (0.22 samples/second). This research highlighted the trade-offs between different VLMs for FFB ripeness assessment, demonstrating their potential to revolutionize the agriculture industry. Future work may focus on optimizing model performance and deploying these technologies in real-world scenarios.

关键词： FFB Large Language Models visual Question Answering Deep Learning image processing

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