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共 79605 条记录，以下是101-110 订阅

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GoLoColor: Towards Global-Local Semantic Aware image Colorization

GoLoColor: Towards Global-Local Semantic Aware Image Coloriz...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yue, Tianai Du, Xiangcheng Liu, Jing Fang, Zhongli Johns Hopkins University BaltimoreMD United States Fudan University Shanghai China Beijing Chest Hospital Capital Medical University Beijing China

ISBN: (纸本)9798350368741

Owing to powerful generative priors, Text-to-image (T2I) diffusion models have achieved promising results in image colorization task. However, recent advanced methods primarily integrate global semantics. Such practice neglects local semantics, yielding suboptimal colorization performance. In this paper, we present a novel global-local semantic aware colorization method named GoLoColor, which performs semantic awareness at both global and local levels. The GoLoColor includes Global Aware (GoA) module, Local Aware module (LoA) and Semantic Aggregation (SA) module for semantic understanding. Specifically, the GoA produces global semantic embedding to represent whole image, while the LoA provides semantic support for local objects, particularly in scenes containing multiple entities. The SA module facilitates semantic interaction between local and global semantic embedding to produce richer semantic information. Finally, a controlled T2I diffusion model is utilized to produce color image guided by the aggregated semantic embedding. Comprehensive experiments demonstrate that our method achieves superior performance and can produce realistic colorization. © 2025 ieee.

关键词： controlled diffusion model global-local semantic awareness image colorization

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Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained image Classification

Hybrid Feature Collaborative Reconstruction Network for Few-...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Qiu, Shulei Yang, Wanqi Yang, Ming Nanjing Normal University Nanjing China

ISBN: (纸本)9798350368741

Our research focuses on few-shot fine-grained image classification (FS-FGIC), which faces two main challenges: the similarity of fine-grained objects and a limited number of samples. Traditional feature reconstruction networks enhance key features through spatial reconstruction and error minimization but often fail to capture inter-class differences with limited samples. We propose a Hybrid Feature Collaborative Reconstruction Network (HFCR-Net) with two key components: the Hybrid Feature Fusion Process (HFFP) and the Hybrid Feature Reconstruction Process (HFRP). In HFRP, dynamic weight adjustment is employed to enhance spatial dependencies and channel correlations, increasing inter-class differences. In HFRP, we introduce channel dimension reconstruction to improve the processes of support-to-query and query-to-support reconstruction, further increasing inter-class and reducing intra-class differences. Extensive experiments on three widely used fine-grained datasets confirm the effectiveness and superiority of our approach. © 2025 ieee.

关键词： feature reconstruction few-shot learning fine-grained image classification

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Lossless Phase Conversion Method for Object Wave-based Hologram Compression

Lossless Phase Conversion Method for Object Wave-based Holog...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Kojima, Hiroki Koiso, Ryota Watanabe, Ryosuke Nonaka, Keisuke KDDI Research Inc. Japan

ISBN: (纸本)9798350368741

An object wave is a suitable intermediate representation format for transmitting holographic application data because it is compatible with various playback devices. However, compressing the phase component of the object wave presents challenges due to its unique signal characteristics, i.e., periodicity and randomness. Hence, object waves significantly differ from ordinary 2D images such as camera-captured and screen pictures. To overcome the challenges, we propose a conversion method for lossless phase image coding using differential pulse-code modulation. Specifically, we calculate phase differences using adjacent pixels in two directions, considering periodic cycles of 2π, and then input them into the existing image coding. This method ensures robustness against randomness by smoothing the phase difference using the average of adjacent pixels. Experimental results show that lossless phase coding with the proposed conversion method achieves coding efficiency improvements of 6.50 % over lossless phase coding without the proposed method. © 2025 ieee.

关键词： Computer-generated hologram Differential pulse-code modulation Lossless image coding Object wave data

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Deformable Attention-Based Edge-Aware Network for Single image Super-Resolution

Deformable Attention-Based Edge-Aware Network for Single Ima...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Ju Zhong, Baojiang Ma, Kai-Kuang School of Computer Science and Technology Soochow University Suzhou China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368741

Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The DAE network features a deformable attention block that dynamically adjusts attention weights to align with edge structures, thereby improving edge awareness and reconstruction quality. Moreover, our network incorporates a multi-patterns window block that captures fine-grained edge details and enhances information flow across network layers. This combination results in visually superior SISR outputs. Extensive experiments and comparisons with the state-of-the-art methods demonstrate that our DAE network excels in both quantitative metrics and visual quality. © 2025 ieee.

关键词： deformable attention edge-aware single image super-resolution window attention

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DARNet: A Dual Attention Residual Network for Medical image Classification

DARNet: A Dual Attention Residual Network for Medical Image ...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Ao Guan, Zhenghua Zhang, Tengda Hu, Wenzheng Liu, Yi Lei, Baiying School of Biomedical Engineering Shenzhen University Shenzhen China Artificial Intelligence Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

In the field of medical image analysis, accurate classification of images is crucial for diagnosing diseases and formulating treatment plans. Many studies have shown that global features and local features help reduce noise interference in medical images. Due to the fixed receptive field size of the convolution kernel, it is difficult to capture the global features of the image. Although the self-attention-based Transformer can model long-range dependencies, it has high computational complexity and lacks local inductive bias. This paper proposes a new module based on dual attention, the Dual Attention Residual Module(DARNet), which uses multi-head self-attention (MHSA) to enhance the global feature extraction ability of convolutional neural network (CNN), while the convolutional block attention module (CBAM) enhances local feature extraction, and then fuses features at different levels through dual-step attention fusion (DSAF). Then we use the architecture of resnet to build the network-Dual Attention Residual Network (DARNet). We evaluate our network on the ISIC2018 and Kvasir datasets and demonstrate its superior performance compared with state-of-the-art models. © 2025 ieee.

关键词： endoscopic gastrointestinal diseases feature fusion medical image classification skin cancer

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When CLIP Meets PHOC: A Dual-Branch Network for Historical Document image Retrieval

When CLIP Meets PHOC: A Dual-Branch Network for Historical D...

引用

2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Zhang, Jing Wei, Hongxi Zhang, Qing School of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9798350368741

In this paper, we leverage Contrastive Language-image Pre-training (CLIP) for Historical Document image Retrieval (HDIR). We are largely inspired by recent advances on CLIP and its exceptional generalization capabilities, but for the first time, we tailor it to benefit HDIR. We put forward a dual-branch joint learning network based on CLIP and Pyramid Histograms of Characters (PHOC), termed CPNet, which supports out-of-vocabulary word spotting for both query-by-example and query-by-string. To achieve this, we introduce two key components: (i) a rule-based PHOC to enhance text representations by capturing detailed character-level information, and (ii) a visual prompt that allows the CLIP image encoder to adapt and extend its knowledge to our task through additional learnable prompts. Extensive experiments on benchmark datasets, including Kanjur and Geser, demonstrate that the proposed CPNet sets a new state-of-the-art in performance for HDIR, highlighting its potential for advancing research in other text-related retrieval tasks. © 2025 ieee.

关键词： CLIP historical documents images retrieval out-of-vocabulary PHOC

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Amplitude-Guidance Low-Light image Enhancement with Frequency-based Channel Attention

Amplitude-Guidance Low-Light Image Enhancement with Frequenc...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Jinlong Tang, Xiongxin Xu, Fanjiang Yang, Hanxiang Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350368741

Low-light image enhancement aims to improve lightness and eliminate degradation caused by low light. However, most current methods struggle to effectively handle the mixed degradations of both brightness and structure, leading to structural distortions and insufficient brightness enhancement. Additionally, existing Fourier-based methods learn amplitude and phase independently, yet overlook the intrinsic connection between brightness and structure. In this paper, we propose an amplitude-guidance low-light image enhancement network, which utilizes the Fourier transform to extract the amplitude and phase of images and reconstruct them using the network. Considering that uneven brightness distribution in images can lead to varying levels of structural degradation, we design an Amplitude-Guidance Self-Attention (AGSA) that uses amplitude to guide phase recovery, enabling it to handle different levels of structure degradation. Additionally, to further improve the enhancement capability of our network, we design a Frequency-based Channel Attention (FCA) that preserves more frequency information when compressing channels. Extensive experiments demonstrate the superiority of our proposed network over existing SOTA methods. © 2025 ieee.

关键词： Channel Attention Fourier Transform Low-light image Enhancement Self-Attention

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Segmentation-Guided Sparse Transformer for Under-Display Camera image Restoration

Segmentation-Guided Sparse Transformer for Under-Display Cam...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Xue, Jingyun Wang, Tao Dai, Pengwen Zhang, Kaihao Sun Yat-sen University Shenzhen China Nanjing University Nanjing China Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (纸本)9798350368741

Under-display Camera is an emerging technology for full-screen display with a camera under the display. However, the current implementation of UDC causes serious image degradation. Incident light required for camera imaging undergoes attenuation and diffraction when passing through the display. Current UDC image restoration methods predominantly utilize convolutional networks, whereas transformer-based methods with superior performance are lacking. This paper proposes a Segmentation-Guided Sparse Transformer method (SGSFormer) for restoring images from UDC degraded images. Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model's attention to focus on the features more relevant to the degraded regions in need of reconstruction. Moreover, we integrate an instance segmentation map as prior information to guide sparse self-attention in filtering and focusing on the correct regions. Extensive experiments exhibit the superior performance of our model over the state-of-the-art methods. © 2025 ieee.

关键词： computer vision image restoration transformer

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DEFormer: DCT-driven Enhancement Transformer for Low-light image and Dark Vision

DEFormer: DCT-driven Enhancement Transformer for Low-light I...

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2025 ieee international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Yin, Xiangchen Yu, Zhenda Gao, Xin Sun, Xiao Institute of Artificial Intelligence Hefei Comprehensive National Science Center University of Science and Technology of China Hefei230601 China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230601 China School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350368741

Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance. © 2025 ieee.

关键词： Discrete Cosine Transform Frequency Learning Low-light image Enhancement Transformer

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Optimizing Multimodal Transformers for Medical image Captioning: Enhancing Automated Descriptions via AI Systems 6

Optimizing Multimodal Transformers for Medical Image Caption...

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6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Arman, Mithila Jahan, Md. Khurshid Dhrubo, Ahmed Faizul Haque Rhaman, Md. Mahfuzur Choya, Sumaya Binte Zilani Dohan, Din Mohammad Ul Islam Sajid, Md. Ashiq Rabiul Alam, Md. Golam BRAC University Dept. of CSE Dhaka Bangladesh North South University Dept. of ECE Dhaka Bangladesh George Mason University Dept. of Computational Data Science FairfaxVA United States American International University-Bangladesh Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798331506520

In contemporary diagnostic workflows, medical image captioning has emerged as a pivotal advancement, combining deep learning methodologies and transformer architectures to enhance accuracy and efficiency in medical interpretations. This paper proposes the optimization of multimodal transformers for automated medical image captioning, focusing on integrating Vision Transformers (ViT) and Bidirectional Auto-Regressive Transformers (BART) with novel variations such as Swin Transformers and GPT-2. We use a robust multimodal AI framework to explore how these architectures synergize to generate coherent and diagnostically relevant captions for radiological images. We assess the performance of multiple transformer models by employing the ROCO dataset, containing paired X-ray images and expert-generated reports. Our findings demonstrate that the ViT + BART combination yields the most stable and accurate captions, minimizing training and validation loss. In contrast, DEiT + MBART displayed instability, highlighting the need for further hyperparameter tuning. Through this comparative analysis, we underscore the critical role of transformer-based models in reducing the cognitive load on medical professionals, enhancing diagnostic accuracy, and promoting real-time, automated medical image interpretation. © 2025 ieee.

关键词： Medical imaging

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