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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79605 条记录，以下是151-160 订阅

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Enhancing image Generation Fidelity via Progressive Prompts

Enhancing Image Generation Fidelity via Progressive Prompts

引用

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

作者： Xiong, Zhen Li, Yuqi Yang, Chuanguang Tan, Tiao Zhu, Zhihong Li, Siyuan Ma, Yue Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China Peking University China EaseUS China The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798350368741

Diffusion transformer (DiT) architecture catches much attention in image generation, which achieves better fidelity, performance, and diversity. However, most existing DiT-based image generation methods are global-aware synthesis and regional prompt control is less explored. In this paper, we propose a coarse-to-fine generation pipeline for regional prompt-following generation. Specifically, we first leverage the powerful large language model (LLM) to generate the high-level description of image (such as content, topic, and objects) and low-level description of image (such as details and style). Then we explore the influence of cross-attention layers in different depths. We discover that deeper layers always responsible for the high-level content control, while the shallow layers handles low-level content control. The various prompts are injected into the proposed regional cross-attention control in order for course-to-fine generation. Using the proposed pipeline, we improve the controllability of DiT-based image generation. Extensive quantitative and qualitative results demonstrate that our pipeline enables to improve the generated performance. Our codes are available at https://***/ZhenXiong-dl/ICASSP2025-RCAC. © 2025 ieee.

关键词： Diffusion model Diffusion transformer Text-to-image generation

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DDSP Guitar Amp: Interpretable Guitar Amplifier Modeling

DDSP Guitar Amp: Interpretable Guitar Amplifier Modeling

引用

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

作者： Yeh, Yen-Tung Chen, Yu-Hua Cheng, Yuan-Chiao Wu, Jui-Te Fu, Jun-Jie Yeh, Yi-Fan Yang, Yi-Hsuan National Taiwan University Taipei Taiwan Positive Grid Taipei Taiwan

ISBN: (纸本)9798350368741

Neural network models for guitar amplifier emulation, while being effective, often demand high computational cost and lack interpretability. Drawing ideas from physical amplifier design, this paper aims to address these issues with a new differentiable digital signal processing (DDSP)-based model, called "DDSP guitar amp, " that models the four components of a guitar amp (i.e., preamp, tone stack, power amp, and output transformer) using specific DSP-inspired designs. With a set of time- and frequency-domain metrics, we demonstrate that DDSP guitar amp achieves performance comparable with that of black-box baselines while requiring less than 10% of the computational operations per audio sample, thereby holding greater potential for usages in real-time applications. © 2025 ieee.

关键词： differentiable digital signal processing Guitar amplifier modeling virtual analog modeling

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AMNS: Attention-Weighted Selective Mask and Noise Label Suppression for Text-to-image Person Retrieval

AMNS: Attention-Weighted Selective Mask and Noise Label Supp...

引用

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

作者： Zhang, Runqing Zhou, Xue Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)9798350368741

Most existing text-to-image person retrieval methods usually assume that the training image-text pairs are perfectly aligned;however, the noisy correspondence(NC) issue (i.e., incorrect or unreliable alignment) exists due to poor image quality and labeling errors. Additionally, random masking augmentation may inadvertently discard critical semantic content, introducing noisy matches between images and text descriptions. To address the above two challenges, we propose a noise label suppression method to mitigate NC and an Attention-Weighted Selective Mask (AWM) strategy to resolve the issues caused by random masking. Specifically, the Bidirectional Similarity Distribution Matching (BSDM) loss enables the model to effectively learn from positive pairs while preventing it from over-relying on them, thereby mitigating the risk of overfitting to noisy labels. In conjunction with this, Weight Adjustment Focal (WAF) loss improves the model's ability to handle hard samples. Furthermore, AWM processes raw images through an EMA version of the image encoder, selectively retaining tokens with strong semantic connections to the text, enabling better feature extraction. Extensive experiments demonstrate the effectiveness of our approach in addressing noise-related issues and improving retrieval performance. © 2025 ieee.

关键词： cross-modal retrieval image mask noise suppression Text-to-image person retrieval

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SelfDeblur with Sparsity Enforced Bregman Learning

SelfDeblur with Sparsity Enforced Bregman Learning

引用

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

作者： Zeng, Yaoyun Chen, Beier Wang, Hongxia Department of Mathematics National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Learning-driven methods have revolutionized the field of blind deconvolution, with SelfDeblur as a pioneering method. It uses deep image priors to jointly estimate blur kernels and latent clear images, employing deep neural networks to automatically learn these priors and effectively capture their statistical properties. Building on the success of SelfDeblur, this paper proposes a novel sparse optimization method, named B-SelfDeblur, which employs linearized Bregman iterations to train neural networks. Our approach addresses the challenges of processing large-scale blurry images by starting with minimal parameters and incrementally adding necessary ones in an inverse scale space manner. Additionally, we incorporate a smoothing technique for the total variation (TV) regularization term, which ensures our loss function is continuously differentiable. Experimental results demonstrate that B-SelfDeblur can significantly optimize network weight storage while maintaining deblurring effectiveness. Furthermore, we provide a partial theoretical analysis and ablation studies, which confirm that our loss function is monotonically decreasing and that our method is well-suited for alternating iterative optimization. © 2025 ieee.

关键词： Blind deconvolution Bregman learning deep image priors SelfDeblur

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CT image Prediction Of PD-1 Gastric Cancer Patients Based On The PLSG Framework

CT Image Prediction Of PD-1 Gastric Cancer Patients Based On...

引用

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

作者： Yuan, Chaoyu Wang, Nan Wang, Mohan Xue, Yingwei College of Computer Science and Technology Heilongjiang University Harbin China Harbin Medical University Cancer Hospital Harbin China

ISBN: (纸本)9798350368741

Survival prediction in PD-1 inhibitor patients has received extensive attention in recent years. Existing diffusion models generally focus blurring on key lesion regions, and the masks are weakly matched to CT images during the sampling process, resulting in a relatively high risk of misdiagnosis. For this reason, a new framework for late survival prediction of gastric cancer (PLSG) is designed in this paper. A mask-based adaptive diffusion algorithm is proposed for enhancing the characterization of critical regions in medical images. A three-domain feature mapping module based on a self-attention mechanism is designed to efficiently acquire higher-order features of images. A sparsification-based principle is utilized to select high-quality features in the prediction stage, which in turn achieves the final screening and prediction of features. Numerous experiments show that the model has greater value in PD-1 treatment. © 2025 ieee.

关键词： CT image processing Diffusion Model Late-stage Gastric Cancer PD-1 Associated Patients Survival Prediction

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Certainty-guided Reasoning and Refinement Network for Camouflaged Object Detection

Certainty-guided Reasoning and Refinement Network for Camouf...

引用

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

作者： Lai, Bifan Sun, Meijun Zhao, Junkun Zhou, Yan College of Intelligence and Computing Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (纸本)9798350368741

Camouflaged object detection (COD), which aims to segment objects that are highly similar to their background, is a valuable yet challenging task. Due to the interference of clutter and noise in the background, existing methods often struggle to avoid misleading and accurately segment the camouflaged object. In this paper, we propose a novel Certainty-guided Reasoning and Refinement Network (CRRNet) for COD. The core idea is to first explicitly reason under accurate knowledge of camouflaged objects, and then refine the uncertain regions by a certainty propagation strategy. To achieve this, we innovatively propose two modules: the Certainty-guided Reasoning Module and Uncertain Region Refinement Module. Experimental results prove that the proposed CRRNet outperforms 12 state-of-the-art methods on three COD benchmark datasets. © 2025 ieee.

关键词： camouflaged object detection certainty-guided reasoning image segmentation

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MS-RainMamba: Learning Multi-Scale State Space Models for Single image Deraining

MS-RainMamba: Learning Multi-Scale State Space Models for Si...

引用

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

作者： Li, Haibo Liu, Zhanshuo Zhao, Tuo Zhao, Tingting Chen, Yarui Xie, Ning College of Artificial Intelligence Tianjin University of Science and Technology Tianjin China Tokyo Japan Center for Future Media University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

Despite the significant advances of Convolutional neural networks (CNNs) and Transformers in image deraining, they either suffer from limited receptive fields or incur quadratic complexity, leading to an imbalance between performance and efficiency. Recently, state space models (SSMs) have demonstrated significant potential in modeling long-range dependencies while maintaining linear complexity. However, existing Mamba-based approaches lack the exploration of useful complementary information from multiple image scales, which could be beneficial for facilitating rain removal. In this paper, we propose an effective multi-scale state-space model-based framework (MS-RainMamba) to explore richer scale-space information for better image deraining. Specifically, we design a local-enhanced state space module to better aggregate rich local and global information. In contrast to existing methods that adopt fixed-scale scanning for feature extraction, we develop a multi-scale hierarchical 2D scanning technique to better help image restoration. Experimental results on six benchmarks show that the proposed method performs favorably against state-of-the-art models. © 2025 ieee.

关键词： image deraining Mamba Multi-scale learning Rain removal State space model

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Research on image optimization of graphic design based on 3D laser vision technology 2

Research on image optimization of graphic design based on 3D...

引用

2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Yang, Dianqing Liu, Renjing Visual Design College Keimyung University Dalseo-gu Daegu704-701 Korea Republic of

ISBN: (纸本)9781510689053

To prevent image distortion, this paper explores methods for enhancing and optimizing graphic design images using 3D laser vision technology. The process involves collecting graphic design image data, mapping 3D laser information onto the surface of the image to form an information map, and utilizing this data for locally weighted complexity processing. A histogram of the graphic design image is constructed, followed by scanning and grayscale transformation to eliminate noise. The image histogram is then segmented using a block-based local enhancement method to achieve the optimization and enhancement of the graphic design image. Experimental results show that under different noise standard deviations, the average peak signal-to-noise ratio (PSNR) of the proposed method is 36.62 dB, and the average structural similarity index (SSIM) is 0.9526. This method effectively reduces noise and enhances the visual quality of graphic design images, improving the ability of the human eye to discriminate information in these images. © 2025 SPIE.

关键词： image enhancement

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D2-MLP: Dynamic Decomposed MLP Mixer for Medical image Segmentation

D2-MLP: Dynamic Decomposed MLP Mixer for Medical Image Segme...

引用

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

作者： Yang, Jin Yang, Jing Yu, Xiaobing Qiu, Peijie Prajapat, Sunil Mallinckrodt Institute of Radiology Washington University School of Medicine St. Louis United States Department of Computer System and Technology University of Malaya Kuala Lumpur Malaysia Central University of Himachal Pradesh Dharamshala India

ISBN: (纸本)9798350368741

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it uses novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods. © 2025 ieee.

关键词： Channel and Spatial Features Dynamic Networks Medical image Segmentation MLP Mixer

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EqGAN: Reformation-based Feature Equalization Fusion for Few-shot image Generation

EqGAN: Reformation-based Feature Equalization Fusion for Few...

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

作者： Zhou, Yingbo Yue, Zhihao Ye, Yutong Zhang, Pengyu Wei, Xian Chen, Mingsong MoE Eng. Research Center of SW/HW Co-Design Tech. and App. East China Normal University China

ISBN: (纸本)9798350368741

Due to the absence or mismatch of semantic information, existing few-shot image generation methods suffer from unsatisfactory generation quality and diversity, which have minimal benefits as data augmentation for downstream classification tasks. Reformatting the contextual and textural information of features at different scales, we propose a novel Feature Equalization Fusion Generative Adversarial Network (EqGAN) for few-shot image generation. Specifically, we first decompose the encoded features into textual and structural components to mitigate the influence of irrelevant and redundant information. Based on feature correlation learning and attention mechanism, we then obtain fused features by refining different contents (i.e., textures and structures) with a more fine-grained semantic alignment. Moreover, an attention-based reconstruction loss and a consistency-based equalization loss are devised to provide better training stability and generation performance. Comprehensive experiments on three public datasets demonstrate that EqGAN not only significantly improves the FID scores (by up to 14.10%) and LPIPS scores (by up to 3.17%) of generated images, but also outperforms the state-of-the-art in terms of accuracy (by up to 3.89%) for downstream classification. © 2025 ieee.

关键词： Equalization Fusion Few-shot image Generation Generative Adversarial Network

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