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检索条件"任意字段=2024 Conference on Visual Communications and Image Processing"

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FSDN: image frequency and semantic decomposition network for image dehazing

FSDN: Image frequency and semantic decomposition network for...

引用

2024 conference on visual communications and image processing

作者： Tong, Zongyang Liu, Mingyu Jin, Xin Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

image dehazing plays a crucial role in autonomous driving and outdoor surveillance. However, as haze affects different components of an image in various ways and degrees, existing methods treat the image as a singular input and overlook the need to decouple different components, leading to mutual interference during the enhancement of each component. Consequently, issues such as insufficient color restoration or blurred edges may arise. In this paper, we introduce a novel tri-branch network for Single image Dehazing that independently extracts low-frequency, high-frequency, and semantic information from images using three distinct sub-networks. A meticulously designed fusion network is then employed to integrate the information from these three branches to produce the final dehazed image. To facilitate the training of such a complex network, we propose a two-stage training approach. Experimental results demonstrate that our approach achieves state-of-the-art (SOTA) performance.

关键词： deep learning dehazing transformer

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Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-Based Methods 10th

Lymphoma Tumor Coding and Information Extraction: A Comparat...

引用

10th China Health Information processing conference, CHIP 2024

作者： Hou, Jian Yang, Fen Chen, Weihua Wang, Yining Feng, Jing Shi, Jin Fan, Jiahui Li, Jianlin Gu, Jinmin Lv, Siyu Cen, Yingying Unisound AI Technology Co. Ltd. Beijing100096 China The Eighth Affiliated Hospital Sun Yat-sen University Shenzhen518033 China

ISBN: (纸本)9789819642977

This study addresses the challenges of information extraction and complete tumor registry coding for lymphoma [1], a malignancy originating from the lymphatic system. Lymphoma encompasses various subtypes, primarily classified into Hodgkin lymphoma and non-Hodgkin lymphoma, both of which exhibit a high incidence globally. Accurate coding of lymphoma diagnoses is particularly challenging due to the complexity and diversity of its subtypes, each possessing unique clinical features and diagnostic criteria. The research explores the potential of large language models (LLMs) in automating the coding process for lymphoma clinical diagnoses by leveraging extensive medical literature, case reports, and clinical guidelines. By simulating the cognitive processes of human coders, these models can offer personalized coding suggestions, thereby enhancing the accuracy and efficiency of diagnostic coding. Through a comparative analysis of coding results generated by LLMs and human coders, this study aims to evaluate the strengths and limitations of these models, ultimately promoting their application in clinical practice. We employed a pipeline with lymphoma info extraction, GraphRAG, and mutual mapping for accuracy enhancement. Our UniGPT model, trained on bilingual and medical data, was assessed for effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image retrieval

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Perceptual image Compression With Conditional Diffusion Transformers

Perceptual Image Compression With Conditional Diffusion Tran...

引用

2024 conference on visual communications and image processing

作者： Mao, Rui Feng, Xinmin Gao, Changsheng Li, Li Liu, Dong Sun, Xiaoyan Univ Sci & Technol China MOE Key Lab Brain Inspired Intelligent Percept & Hefei Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Generative models have significantly advanced generative AI, particularly in image and video generation. Recognizing their potential, researchers have begun exploring their application in image compression. However, existing methods face two primary challenges: limited performance improvement and high model complexity. In this paper, to address these two challenges, we propose a perceptual image compression solution by introducing a conditional diffusion model. Given that compression performance heavily depends on the decoder's generative capability, we base our decoder on the diffusion transformer architecture. To address the model complexity problem, we implement the diffusion transformer architecture with Swin transformer. Equipped with enhanced generative capability, we further augment the decoder with informative features using a multi-scale feature fusion module. Experimental results demonstrate that our approach surpasses existing perceptual image compression methods while achieving lower model complexity.

关键词： Diffusion transformer (DiT) image generation perceptual image compression Swin transformer

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Cross-Device image Saliency Detection: Database and Comparative Analysis

Cross-Device Image Saliency Detection: Database and Comparat...

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2024 conference on visual communications and image processing

作者： Ding, Xiaoying Yue, Guanghui Zhang, Yingxue Zhongnan Univ Econ & Law Sch Informat Engn Wuhan Peoples R China Shenzhen Univ Med Sch Sch Biomed Engn Shenzhen Peoples R China Tianjin Univ Sci & Technol Coll Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

While saliency detection for images has been extensively studied during the past decades, only a little work explores the influence of different viewing devices (i.e., tablet computer, mobile phone) towards human visual attention behavior. The lack of research in this field hinders the research progress in cross-device image saliency detection. In this paper, we first establish a novel cross-device saliency detection (CDSD) database based on eye-tracking experiments and investigate subjects' visual attention behavior when using different viewing devices. Then, we evaluate several classic saliency detection models using the CDSD database and the evaluation results indicate that the cross-device performance of these models need further improvement. Finally, some meaningful discussions are provided which might enlighten the design of cross-device saliency detection model. The proposed CDSD database will be made publicly available.

关键词： cross-device saliency detecton visual attention eye-tracking

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ALICE: Adapt your Learnable image Compression modEl for variable bitrates

ALICE: Adapt your Learnable Image Compression modEl for vari...

引用

2024 conference on visual communications and image processing

作者： Spadaro, Gabriele Ali, Muhammad Salman Presta, Alberto Pilo, Giommaria Bae, Sung-Ho Giraldo, Jhony H. Fiandrotti, Attilio Grangetto, Marco Tartaglione, Enzo Inst Polytech Paris Telecom Paris LTCI Paris France Univ Turin Turin Italy Kyung Hee Univ Yongin South Korea

ISBN: (纸本)9798331529543;9798331529550

When training a Learned image Compression model, the loss function is minimized such that the encoder and the decoder attain a target Rate-Distorsion trade-off. Therefore, a distinct model shall be trained and stored at the transmitter and receiver for each target rate, fostering the quest for efficient variable bitrate compression schemes. This paper proposes plugging Low-Rank Adapters into a transformer-based pre-trained LIC model and training them to meet different target rates. With our method, encoding an image at a variable rate is as simple as training the corresponding adapters and plugging them into the frozen pre-trained model. Our experiments show performance comparable with state-of-the-art fixed-rate LIC models at a fraction of the training and deployment cost. We publicly released the code at https://***/EIDOSLAB/ALICE.

关键词： image compression

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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

引用

2024 conference on visual communications and image processing

作者： Fu, Nianxiang Chen, Zhenzhong Wang, Huairui Liu, Shan Wuhan Univ Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Shenzhen Peoples R China Tencent Amer Palo Alto CA USA

ISBN: (纸本)9798331529543;9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Learned image compression quantization error compensator quantization error

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Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural image Compression

Semantic Ensemble Loss and Latent Refinement for High-Fideli...

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2024 conference on visual communications and image processing

作者： Li, Daxin Bai, Yuanchao Wang, Kai Jiang, Junjun Liu, Xianming Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Recent advancements in neural compression have surpassed traditional codecs in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can introduce visually displeasing artifacts, such as blurring, color shifting, and texture loss, thereby compromising perceptual quality of images. To address these issues, this study presents an enhanced neural compression method designed for optimal visual fidelity. We have trained our model with a sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual loss, style loss, and a non-binary adversarial loss, to enhance the perceptual quality of image reconstructions. Additionally, we have implemented a latent refinement process to generate content-aware latent codes. These codes adhere to bit-rate constraints, and prioritize bit allocation to regions of greater importance. Our empirical findings demonstrate that this approach significantly improves the statistical fidelity of neural image compression.

关键词： Generative image Compression Learned image Compression

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Coarse-to-fine Transformer For Lossless 3D Medical image Compression

Coarse-to-fine Transformer For Lossless 3D Medical Image Com...

引用

2024 conference on visual communications and image processing

作者： Yang, Xiaoxuan Lui, Guo Feng, Donghui Cheng, Zhengxue Yu, Guosheng Song, Li Shanghai Jiao Tong Univ Shanghai Peoples R China Alibaba Grp T Head Semicond Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The rapid advancements in medical imaging have led to a growing demand for high-performance lossless compression of large 3D medical image datasets. Unlike natural images, medical images typically feature three-dimensional structures, and high bit-depth, necessitating specialized compression techniques. Based on a decoder-only transformer, we propose a learnable dual-decoder model for lossless compression of 3D medical images. Our approach packs voxels into patches, which are processed by a patch-level decoder to extract the patch feature. The voxels, along with the patch feature, are subsequently fed into a voxel-level decoder to model each voxel. This coarse-to-fine modeling strategy reduces the computational time for each voxel and enables long-range modeling dependencies. Experimental results demonstrate that our proposed model achieves state-of-the-art compression performance, with an approximately 15% improvement in compression performance over the traditional JP3D benchmark on various datasets.

关键词： Lossless image Compression Medical image Transformer

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image Forensics Strikes Back: Defense Against Adversarial Patch

Image Forensics Strikes Back: Defense Against Adversarial Pa...

引用

2024 conference on visual communications and image processing

作者： Kao, Ching-Chia Lu, Chun-Shien Yu, Chia-Mu Acad Sinica Inst Informat Sci IIS Taipei Taiwan Natl Taiwan Univ Taipei Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798331529543;9798331529550

Traffic sign recognition plays a crucial role in self-driving cars, but unfortunately, it is vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models in previous studies, the connection between image forensics and AP detection still needs to be explored. From a high-level point of view, their goals are the same. That is to find tampered regions and prevent false positives in the meantime. A natural question arises: "Is achieving application-agnostic anomaly detection possible?" In this paper, we propose image Forensics Defense Against Adversarial Patch (IDAP), a framework to defend against adversarial patches via generalizable features learned from tampered images. In addition, we incorporate the Hausdorff erosion loss into our network model for joint training to complete the shape of a predicted mask. Extensive experimental comparisons on three datasets, including COCO, DFG, and APRICOT demonstrate that IDAP outperforms state-of-the-art AP detection methods.

关键词： Adversarial Patch AI Security image Forensics

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Codec-agnostic Lenslet Video Coding with Smoothing Transform

Codec-agnostic Lenslet Video Coding with Smoothing Transform

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2024 conference on visual communications and image processing

作者： Soetens, Eline Lafruit, Gauthier Teratani, Mehrdad Univ Libre Bruxelles Lab Image Synth & Anal Brussels Belgium

ISBN: (纸本)9798331529543;9798331529550

Plenoptic cameras are light field capturing devices able to acquire large amounts of angular and spatial information. The lenslet video produced by such cameras presents on each frame a distinctive hexagonal pattern of micro-images. Due to the particular structure of lenslet images, traditional video codecs perform poorly on lenslet video. Previous works have proposed a preprocessing scheme that cuts and realigns the micro-images on each lenslet frame. While effective, this method introduces high frequency components into the processed image. In this paper, we propose an additional step to the aforementioned scheme by applying an invertible smoothing transform. We evaluate the enhanced scheme on lenslet video sequences captured with single-focused and multi-focused plenoptic cameras. On average, the enhanced scheme achieves 9.85% bitrate reduction compared to the existing scheme.

关键词： Video signal processing

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