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共 79605 条记录，以下是71-80 订阅

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LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Generation

LocRef-Diffusion:Tuning-Free Layout and Appearance-Guided Ge...

引用

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

作者： Deng, Fan Wu, Yaguang Yu, Xinyang Huang, Xiangjun Yang, Jian Yan, Guangyu Xu, Qiang MetaX Shanghai China

ISBN: (纸本)9798350368741

Recently, text-to-image models based on diffusion have achieved remarkable success in generating high-quality images. However, the challenge of personalized, controllable generation of instances within these images remains an area in need of further development. In this paper, we present LocRef-Diffusion, a novel, tuning-free model capable of personalized customization of multiple instances' appearance and position within an image. To enhance the precision of instance placement, we introduce a Layout-net, which controls instance generation locations by leveraging both explicit instance layout information and an instance region cross-attention module. To improve the appearance fidelity to reference images, we employ an appearance-net that extracts instance appearance features and integrates them into the diffusion model through cross-attention mechanisms. We conducted extensive experiments on the COCO and Openimages datasets, and the results demonstrate that our proposed method achieves state-of-the-art performance in layout and appearance guided generation. © 2025 ieee.

关键词： personalization text-to-image tuning-free

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DN-DR: Discriminative Network with Dual Reconstruction for image Anomaly Detection

DN-DR: Discriminative Network with Dual Reconstruction for I...

引用

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

作者： Li, Wen Zhang, Chune Institute of Information Science Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350368741

One mainstream of image anomaly detection is based on reconstruction. Such methods still struggle with diverse anomalies, such as near-in-distribution or deformed types. To address the challenge, we propose a Discriminative Network with Dual Reconstruction (DN-DR), consisting of a Memory Reconstructor, a Corrector, and a Discriminator. DN-DR aims to better restore the defective image to its normal state through dual reconstruction, thereby obtaining superior Discriminator performance. Specifically, (1) the Memory Reconstructor is based on training multi-scale codebooks from normal images to rebuild unknown regions in the test images, also named preliminary reconstruction;(2) the Corrector, as a subsequent reconstruction module, addresses false anomalies caused by the patch-level replacement strategy in the Memory Reconstructor, achieving a final refined reconstruction;(3) a U-Net Discriminator follows. Experiments on the challenging MVTec AD dataset demonstrate excellent reconstruction performance and anomaly inspection, including defects of near-in-distribution or deformed types. © 2025 ieee.

关键词： anomaly detection dual reconstruction reconstruction-based methods

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Integrated Global-Local Gaussian Attention for image Compression

Integrated Global-Local Gaussian Attention for Image Compres...

引用

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

作者： Khoshkhahtinat, Atefeh Mehta, Piyush M. Department of Computer Science and Electrical Engineering West Virginia University WV United States

ISBN: (纸本)9798350368741

Transformers have demonstrated outstanding performance in learned image compression (LIC) due to their high capacity for modeling complex dependencies. However, existing methods employ window-based attention mechanisms to reduce overhead, which limits the long-range dependency modeling capabilities of transformers. In this study, we present a novel transformer block designed to efficiently leverage global spatial information while keeping computational costs low. Additionally, our proposed transformer incorporates a local attention module that utilizes Gaussian positional encoding to enhance spatial awareness. These improvements contribute to achieving less redundant latent representations and improving compression efficiency. In addition to enhance the decorrelating capability of the transformation component, we propose a novel channel-wise entropy model that effectively leverages channel dependencies, thereby providing a more accurate estimation of the latent space distribution. Experimental results indicate that our framework surpasses both conventional and neural codecs. © 2025 ieee.

关键词： Entropy model Guassian positional encoding Learned image Compression Transformer

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Efficient Object Placement Via LLM and Diffusion Model

Efficient Object Placement Via LLM and Diffusion Model

引用

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

作者： Liu, Wei Wang, Liuan Sun, Jun Fujitsu R&D Center Co. Ltd. Peking China

ISBN: (纸本)9798350368741

We address the problem of object placement with user instructions using LLM and diffusion model. Traditional methods struggle to find a suitable location for filling the object with a semantically reasonable size. In this work, we leverage the LLM to predict the coordinates of the added object with the help of user instruction. First, We extend the object placement benchmark OPA-INST for object placement image editing. Second, we propose a framework Inst-GEdit to predict the coordinates of added object and blend the object with background via diffusion model in a semantically consistent and natural manner. Inst-GEdit can accomplish the object placement editing task with the user instruction in a mask free and training free manner. We conduct the experiments and evaluate our method on the OPA and image composition benchmark. We evaluate the results in both subjective and quantitative way, which demonstrate the effectiveness of our proposed method. © 2025 ieee.

关键词： diffusion model image placement LLM user instruction

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Research on the Application of Multi-Channel Display Technology in Virtual Scene image Generation 5

Research on the Application of Multi-Channel Display Technol...

引用

5th ieee international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Liu, Yuanlin Wang, Lingling Haikou University of Economics Haikou571127 China

ISBN: (纸本)9798331533694

This study proposes an innovative algorithm based on DCNN and multi-channel image fusion, aiming to improve the quality and efficiency of virtual scene image generation. The algorithm extracts depth information and texture features in virtual scenes through DCNN, and combines multi-channel image fusion technology to optimize and fuse the information of different image channels (such as color, depth, normal, etc.), thereby significantly improving the image rendering effect. Through adaptive weight adjustment and image fusion strategy, the image detail performance is optimized, especially in scenes with complex lighting and rich textures, the rendering quality is significantly improved. Experimental results show that the image generation system based on this algorithm improves image quality by 15%, rendering speed by 25%, and computing resource consumption by 20%. When processing highly complex virtual scenes, the system can achieve real-time rendering and meet the performance requirements of VR and AR applications. This study provides a new and efficient solution for image generation in virtual reality and augmented reality, and demonstrates the great potential of combining multi-channel display technology with deep learning in improving rendering effects. © 2025 ieee.

关键词： Virtual environments

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

引用

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

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 ieee.

关键词： Frequency domain analysis

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Retinex-Based Self-Conditioned Diffusion Model for Low-Light image Enhancement

Retinex-Based Self-Conditioned Diffusion Model for Low-Light...

引用

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

作者： Zhang, Jiawei Li, Ziwen Zhang, Jinpu Wang, Yuehuan Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional priors to guide the enhancement process, often resulting in unsatisfactory results. To address the issue, we propose Retinex-Based Self-Conditioned Diffusion Models, dubbed RSCDM, which utilizes self-conditioned illumination representation learning and representation guidance enhancement to generate high-quality image. To be specific, in the first stage, we pretrain a retinex decomposed model (RDM) to capture illumination representation and devise a illumination-representation restoration model (IRM) to accurately reconstruct the representation from noisy images. Moreover, we further design dynamic resblock (DRB) and dynamic simplified attention gated block (DSAGB) as basic units of IRM for better fine-grained restoration. In the second stage, we employ a self-conditioned diffusion model (SDM) to generate realistic results conditioned on the illumination representation. Extensive experiments demonstrates our method outperforms the existing SOTA methods both quantitatively and qualitatively. The codes will be publicly available. © 2025 ieee.

关键词： Diffusion Models(DMs) Low Light image Enhancement Retinex-Based Model

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

引用

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

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 ieee.

关键词： denoising Medical image fusion text-guided fusion

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Uncertainty-aware Correspondence Distillation for Deep image Clustering

Uncertainty-aware Correspondence Distillation for Deep Image...

引用

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

作者： Chang, Luyao Chen, Leiting Zhou, Chuan School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350368741

image clustering is a challenging task in computer vision, with performance heavily dependent on the quality of feature representations due to the inherent complexity of images. However, current image clustering methods overlook the underlying semantic information during representation learning, leading to low-quality feature representations. Moreover, the absence of ground-truth labels amplifies the detrimental effects of unreliable data on semantic guidance, steering the model towards incorrect learning directions. In this work, we propose a novel deep image clustering method named Uncertainty-aware Correspondence Distillation (UCD) to address these issues. Specifically, we introduce the concept of representation correspondence to establish cross-level connections between instances and semantics, which is further employed as a distillation target to improve the network's feature learning by complementing semantic information. To mitigate unnecessary similarity penalties arising from unreliable data, we develop robust dynamic weights for semantic guidance by modeling the uncertainty of image semantics. Extensive experiments on five benchmark datasets demonstrate the superiority of the proposed method. The code is available at https://***/YL616/UCD. © 2025 ieee.

关键词： image clustering Knowledge distillation Representation learning Semantic guidance

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Diffusion Model Based image Reconstruction in Lensless Imaging

Diffusion Model Based Image Reconstruction in Lensless Imagi...

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

作者： Verma, Ashish Boominathan, Vivek Veeraraghavan, Ashok Seelamantula, Chandra Sekhar Department of Electrical Engineering Indian Institute of Science Bengaluru560 012 India Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

ISBN: (纸本)9798350368741

Lensless imaging systems eliminate the need for lenses by employing an encoding element to multiplex incident light signals, which are then captured directly onto a bare camera sensor. They present a promising alternative to traditional lens-based imaging systems by offering significant advantages in terms of compactness, versatility, and cost. Due to the multiplexed nature of measurements, image reconstruction takes place computationally. However, existing techniques for image reconstruction in lensless imaging fall short of the image quality offered by traditional lens-based imaging. In this work, we consider the application of diffusion models, a class of deep generative models, for image reconstruction in a lensless imaging modality. These models currently achieve state-of-the-art performance in image generation. Specifically, we focus on the PhlatCam lensless system, which consists of a coded phase mask as the encoding element placed close to the camera sensor. We use a ControlNet based diffusion model to improve the perceptual quality of image reconstruction. The performance is measured in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The proposed method improves the performance in these metrics for synthetic measurements. For real measurements, the improvement in image quality comes at the expense of a small bias in color, which is attributed to the generative nature of the diffusion prior itself. © 2025 ieee.

关键词： Computational imaging diffusion model generative model lensless imaging stable diffusion

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