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Dual Path Unsupervised Real image Denoising

Dual Path Unsupervised Real Image Denoising

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yao Li Zheng-jun Zha College of Cyber Security University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Because of the significant gap between the real noise and the synthetic noise domains, the learning-based method trained on the synthetic noise performs poorly on the task of removing the real noise. To solve the problem, the dual path unsupervised real image denoising method is proposed, by using the unsupervised path of the image translation between the real noise domain and the synthetic noise domain and the indirect supervised path of the image denoising from the realistic noisy images to the noisy-free images. The image translation between the real noise domain and the synthetic noise domain augments the training images, and the effective supervised denoising model is used to tackle with the unsupervised real image denoising problem. This method network structure is flexible to adjust the different tasks in the practical applications. Compared to other unsupervised real image denoising methods, our method can adaptively generate the synthetic noise that shares a similar distribution as the real noise. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method.

关键词： Training Learning systems Bridges Translation Noise Noise reduction Collaboration Noise measurement Speech processing image denoising

来源：评论

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VisAgent: Narrative-Preserving Story Visualization Framework

VisAgent: Narrative-Preserving Story Visualization Framework

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Seungkwon Kim GyuTae Park Sangyeon Kim Seung-Hun Nam NAVER WEBTOON AI Republic of Korea NAVER WEBTOON AI Seoul National University Republic of Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Story visualization is the transformation of narrative elements into image sequences. While existing research has primarily focused on visual contextual coherence, the deeper narrative essence of stories often remains overlooked. This limitation hinders the practical application of these approaches, as generated images frequently fail to capture the intended meaning and nuances of the narrative fully. To address these challenges, we propose VisAgent, a training-free multi-agent framework designed to comprehend and visualize pivotal scenes within a given story. By considering story distillation, semantic consistency, and contextual coherence, VisAgent employs an agentic workflow. In this workflow, multiple specialized agents collaborate to: (i) refine layered prompts based on the narrative structure and (ii) seamlessly integrate generated elements, including refined prompts, scene elements, and subject placement, into the final image. The empirically validated effectiveness confirms the framework’s suitability for practical story visualization applications.

关键词： Visualization Semantics Coherence signal processing Generators Acoustics image sequences Speech processing

来源：评论

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Robust and Efficient Adversarial Defense in SNNs via image Purification and Joint Detection

Robust and Efficient Adversarial Defense in SNNs via Image P...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weiran Chen Qi xu School of Microelectronics University of Science and Technology of China Hefei

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking neural networks (SNNs) leverage neural spikes to provide solutions for low-power intelligent applications on neuromorphic hardware. Although the spiking mechanism significantly enhances computational efficiency, especially in energy-constrained environments, they still lack resistance to noise perturbations and adversarial attacks. In this paper, we propose a defense framework based entirely on SNNs and design a fast and efficient training algorithm. The framework is divided into an image purification module and an adversarial detection module. The image purification module is employed for the extraction of noise and the reconstruction of input images. The adversarial detection module is utilized to differentiate between clean and adversarial images, thereby further enhancing defense performance. Meanwhile, our approach is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time and resource consumption.

关键词： Training Resistance Purification Perturbation methods Noise signal processing algorithms Spiking neural networks Robustness Computational efficiency Speech processing

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IOVS4NeRF: Incremental Optimal View Selection for Large-Scale NeRFs

IOVS4NeRF: Incremental Optimal View Selection for Large-Scal...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jingpeng xie Shiyu Tan Yuanlei Wang Tianle Du Yifei xue Yizhen Lao Hunan University Changsha China Nanchang University Nanchang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.

关键词： Training Uncertainty Training data signal processing Neural radiance field Rendering (computer graphics) Robustness Trajectory Speech processing image reconstruction

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Improved image Classification with Manifold Neural Networks

Improved Image Classification with Manifold Neural Networks

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Caio F. Deberaldini Netto Zhiyang Wang Luana Ruiz Department of Applied Mathematics and Statistics Mathematical Institute for Data Science (MINDS) Data Science and Artificial Intelligence Institute (DSAI) Johns Hopkins University Baltimore USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs’ message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs’ potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks.

关键词： Manifolds Electric potential High dimensional data Autoencoders Transportation signal processing Graph neural networks Molecular biology Speech processing image classification

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Evaluating the Posterior Sampling Ability of Plug&Play Diffusion Methods in Sparse-View CT

Evaluating the Posterior Sampling Ability of Plug&Play Diffu...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Liam Moroy Guillaume Bourmaud Frédéric Champagnat Jean-François Giovannelli DTIS/ONERA Univ. Paris Saclay France IMS (Univ. Bordeaux – CNRS – BINP) France

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be concentrated around a single mode, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer concentrated or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation properties. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.

关键词： Measurement image coding Computed tomography signal processing Diffusion models Acoustics Speech processing image reconstruction

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing Remote sensing

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From Pixels to Voice: A Simple and Efficient End-to-End Spoken image Description Approach via Vision Codec Language Models

From Pixels to Voice: A Simple and Efficient End-to-End Spok...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chung Tran Sakriani Sakti Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Neural audio codecs provide a powerful tool for compressing audio signals into discrete codec representations. This compact discrete representation has made it possible to successfully apply a natural language processing (NLP) model to various audio and speech processing tasks, including text-to-speech (e.g., VALL-E, VALL-E x) and multimodal audio-text generation (e.g., LauraGPT, VioLA). While these models excel at handling sequential data like text and speech, their potential for processing non-sequential data, such as images, remains unexplored. In this paper, we introduce PixVoxLM, a simple and efficient end-to-end framework that combines vision-language models with neural audio codecs to tackle the image-to-Speech (I2S) problem. Experiments on the Flickr8k dataset demonstrate that PixVoxLM delivers promising results compared to existing I2S methods. Furthermore, this research is the first to explore a new capability: visual-guided speech completion in I2S model, paving the way for new practical applications in everyday communication, such as speech prompt-based instruction.

关键词： Codecs Speech coding signal processing Natural language processing Data models Acoustics Text to speech Speech processing Synthetic aperture sonar

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FiTGAN: Content Fusion with Style Transformation for Few-shot image Generation

FiTGAN: Content Fusion with Style Transformation for Few-sho...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the semantic entanglement in fusion strategies or unstable training in complicated image transformations, existing few-shot image generation methods still suffer from low generation quality and diversity. To tackle the above problems, we propose a novel fusion- and transformation-based framework named content Fusion with style Transformation Generative Adversarial Network (FiTGAN) for few-shot image generation. The basic assumption is that any image consists of a collection of content-related and style-related features. FiTGAN disentangles internal representations with two independent encoders and combines the fused contents and transformed styles to generate new images. Specifically, we design a multi-scale content fusion strategy and a reparameterized style transformation mechanism to learn more fine-grained semantics without changing category-relevant attributes. Furthermore, we formulate a content reconstruction loss and a style divergence loss to provide better training stability and generation performance. Comprehensive experiments on three well-known datasets demonstrate that FiTGAN can not only produce more realistic and diverse images for few-shot image generation but also achieve better classification accuracy for downstream visual applications with limited data.

关键词： Training Measurement Visualization image synthesis Semantics Transforms signal processing Stability analysis Speech processing image reconstruction

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SPT: Sequence Prompt Transformer for Interactive image Segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Senlin Cheng Haopeng Sun Tao xie Hangyue Zhao Yiqiang Chen Bolei xu xiaoBo Li Ant Group Hangzhou China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China BAIDU Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Topk Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20K-Seq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets.

关键词： image segmentation Accuracy Benchmark testing signal processing Transformers Acoustics Speech processing

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