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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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Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

Let There Be Light: Robust Lensless Imaging Under External I...

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

作者： Eric Bezzam Stefan Peters Martin Vetterli Audiovisual Communications Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interference (other sources, noise, etc.). In this work, we address a prevalent noise source that has not been studied for lensless imaging: external illumination e.g. from ambient and direct lighting. Being robust to a variety of lighting conditions would increase the practicality and adoption of lensless imaging. To this end, we propose multiple recovery approaches that account for external illumination by incorporating its estimate into the image recovery process. At the core is a physics-based reconstruction that combines learnable image recovery and denoisers, all of whose parameters are trained using experimentally gathered data. Compared to standard reconstruction methods, our approach yields significant qualitative and quantitative improvements. We open-source our implementations and a 25K dataset of measurements under multiple lighting conditions.

关键词： Source separation Noise Lighting Interference Reconstruction algorithms Cameras Optics Speech processing image reconstruction Standards

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LFSRDiff: Light Field image Super-Resolution via Diffusion Models

LFSRDiff: Light Field Image Super-Resolution via Diffusion M...

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

作者： Wentao Chao Junli Zhao Fuqing Duan Guanghui Wang School of Artificial Intelligence Beijing Normal University Beijing China College of Computer Science and Technology Qingdao University Qingdao China Department of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Diffusion models have become a rising star in image super-resolution (SR) tasks. However, it is not trivial to apply diffusion models for light field (LF) image SR, which requires maintaining the high-quality visual appearance of each sub-aperture image (SAI) and the angular consistency between the different SAIs. This paper proposes the first diffusion-based LF image SR model, namely LFSRDiff, by incorporating the LF disentanglement mechanism and residual modeling. Specifically, we introduce a disentangled U-Net (Distg U-Net) for diffusion models, enabling improved extraction and fusion of the spatial and angular information in LF images. Furthermore, we leverage residual modeling in diffusion to learn the residual between the upsampled low-resolution and the ground truth high-resolution, which significantly accelerates model training and yields superior results compared to direct learning. Extensive experiments conducted on the five datasets demonstrate the effectiveness of our approach, which can produce realistic SR results and achieve the highest perceptual metric in terms of LPIPS. Code is publicly available at https://***/chaowentao/LFSRDiff.

关键词： Training Measurement visualization Superresolution Stars Signal processing Diffusion models Light fields Data mining Speech processing

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Temporal and Spatial Perception: A Novel Perceptual Rate-Distortion Optimization Method for H.266/VVC Encoding

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ieee Transactions on Circuits and Systems for Video Technology 2025年

作者： Ai, Da Wang, Jiahao He, Ting Yuan, Hui Liu, Ying Ling, Nam Xi'an University of Posts and Telecommunications Center for Image and Information Processing School of Communications and Information Engineering Xi'an710121 China Shandong University School of Control Science and Engineering Jinan250061 China Shandong University Shenzhen-Research Institute Shenzhen518057 China Santa Clara University Department of Computer Science and Engineering Santa ClaraCA95053 United States

Introducing saliency information to mitigate perceptual redundancy and achieve superior compression represents a novel approach to the development of video compression. Existing saliency-based compression coding methods rely on the determination of saliency regions and focus too much on saliency regions while ignoring the perceptible distortion in non-saliency regions. We propose a spatiotemporal visual perceptual rate-distortion optimization (PRDO) algorithm for Versatile Video Coding (H.266/VVC) that is more in line with the human visual system (HVS). Firstly, we establish a linear weighted distortion model based on spatiotemporal and saliency features. The distortion model makes effective use of saliency features while considering image content in non-saliency regions that is still perceptible to the human eye, thereby achieving an overall visual effect that conforms to human subjective perception. Based on this distortion model, we propose a saliency adaptive quantization parameter (SAQP) selection method with a more flexible quantization parameter selection range, adaptively allocating the optimal coding unit quantization parameter according to the saliency regions of the image, ensuring a balanced bitrate allocation between saliency and non-saliency regions. The proposed method is implemented for the first time on the H.266/VVC coding standard, attaining an average bitrate saving of 19.9% across all test sequences and an average PSNR improvement of 2.65 dB in saliency regions compared to VTM16.0. The BD-EWPSNR of the proposed PRDO and SAQP method improves by 1.34 dB and 1.45 dB in the All-Intra and Lowdelay-P encoding modes, respectively. Additionally, the BD-Rate based on EWPSNR is reduced by 25.86% and 33.73%, respectively, with an overall compression coding time saving of 19.76%. The experimental results demonstrate that the proposed method can significantly reduce the bit rate and coding time while improving the subjective perceptive quality, providing a c

关键词： Signal distortion

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Learning Class Prototypes for visual Emotion Recognition

Learning Class Prototypes for Visual Emotion Recognition

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

作者： Jiankun Zhu Sicheng Zhao Jing Jiang Zhaopan Xu Wenbo Tang Hongxun Yao Faculty of Computing Harbin Institute of Technology Harbin China BNRist Tsinghua University Beijing China Siwei Zhilian Technology Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

visual emotion recognition (VER), which aims at understanding humans’ emotional reactions toward different visual stimuli, has attracted increasing attention. However, because of the subjectivity and complex nature of emotion, existing VER methods suffer from one or more of the following problems: 1) semantic gap: the large affective gap between visual clues and the emotional expressions; 2) overfitting: the lack of model robustness due to unclear features in the emotional category samples; 3) label ambiguity: the overlap between categories caused by diverse emotional responses. To address these limitations, we present a novel VER method named ProtoEmotion (PoE), exploring discriminative emotional representations by jointly learning prototypes of textual emotional expressions and visual features. Specifically, text prototypes build explicit textual features for each emotion category by extracting prototypes of learnable prompts from multiple aspects, reducing semantic differences. The visual prototypes capture the most defining image features of each category, providing a more robust and discriminative feature representation, while bringing samples closer together to reduce overfitting. In addition, to alleviate the label ambiguity, we propose a label smoothing algorithm based on the prototype distance. Extensive experiments demonstrate the effectiveness of PoE, which outperforms the state-of-the-art by 1.37% on FI and 1.52% on EmotionROI datasets.

关键词： visualization Emotion recognition Smoothing methods Semantics Prototypes Signal processing algorithms Speech recognition Feature extraction Speech processing Overfitting

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PromptSeg: Learning to Segment Medical image via visual Prompts

PromptSeg: Learning to Segment Medical Image via Visual Prom...

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

作者： Minfan Zhao Ziqi Zhu Jun Shi Zhaohui Wang Junshi Chen Hong An Bing Yan School of Computer Science and Technology University of Science and Technology of China Hefei China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei China The First Affiliated Hospital of USTC University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning has made remarkable medical image segmentation advancements, yet its generalization capability across tasks remains challenging. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the poor generalization capacity of task-specific models on unseen tasks, necessitating domain adaptation or fine-tuning. This typically involves data annotation and network retraining, limiting the application of deep learning in clinical practice. In this study, we propose PromptSeg, an innovative Transformer-based unified segmentation framework for general medical image segmentation tasks. PromptSeg aims to utilize the provided visual prompts to recognize task patterns and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets during inference, our method only requires a few annotated prompt pairs to understand the task and segment the query images without retraining, alleviating the need for large-scale annotation data. The experimental results demonstrate that our method outperforms existing state-of-the-art methods and exhibits high generalization capability on multiple unseen tasks. The source code is available at https://***/MinfanZhao/PromptSeg.

关键词： Deep learning Training image segmentation visualization Annotations Source coding Scalability Transformers Speech processing Biomedical imaging

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Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into visual Cues

Enhancing Vision-Language Tracking by Effectively Converting...

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

作者： Xiaokun Feng Dailing Zhang Shiyu Hu Xuchen Li Meiqi Wu Jing Zhang Xiaotang Chen Kaiqi Huang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current datasets typically contain much more image data than text, limiting the ability of VLT methods to align the two modalities effectively. To address this imbalance, we propose a novel plug-and-play method named CTVLT that leverages the strong text-image alignment capabilities of foundation grounding models. CTVLT converts textual cues into interpretable visual heatmaps, which are easier for trackers to process. Specifically, we design a textual cue mapping module that transforms textual cues into target distribution heatmaps, visually representing the location described by the text. Additionally, the heatmap guidance module fuses these heatmaps with the search image to guide tracking more effectively. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our approach, achieving state-of-the-art performance and validating the utility of our method for enhanced VLT.

关键词： Heating systems visualization Target tracking Grounding Video sequences Training data Transforms Benchmark testing Numerical models Speech processing

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

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

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

作者： Aimei Dong Jingyuan Xu Long Wang Guohua Lv Guixin Zhao Jinyong Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

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.

关键词： visualization image resolution Semantics Noise reduction Noise Pipelines Noise measurement Speech processing image fusion Biomedical imaging

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Cricket Shot Analysis using Conditional Directed Spatio-Temporal Graph networks

Cricket Shot Analysis using Conditional Directed Spatio-Temp...

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International image processing, Applications and Systems conference (IPAS)

作者： Tanu Aneja Brejesh Lall Harsha Malaviya

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Recent Use of Conditional Spatio-temporal Directed Graph Convolutional Networks(Cond ST-DGCN) [8] to represent human pose estimation has significantly helped in capturing varying non-local dependencies between limbs for different actions. This can be immensely helpful in Sports analytics where player pose plays key role in shot evaluation and can help in corrective action. In this article, we propose CondDGCN [8] based framework to explore use of spatial-temporal relation of batsman shot sequences (labelled and annotated 2D cricket dataset [1]) for Cricket shot action recognition by conditioning the graph network on batsman 2D poses. We achieve 97% accuracy for shot recognition and further explore visualization of conditional graph connections to establish importance of particular limbs for shots. The proposed framework uses fine-tuned 2D Pose estimator OpenPose [11] (fine-tuned for cricket dataset [1]) which in turn helps in easy adaptation of our solution to internet cricket videos for shot analytics.

关键词： Limbs Computer vision Graph convolutional networks visual analytics image processing Pose estimation Directed graphs Computer architecture Sports Videos

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Computation Overhead Optimization Dual-Domain Network with Multi-Level Cross-Domain Connections for Sparse-view CT Reconstruction

Computation Overhead Optimization Dual-Domain Network with M...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), ieee International conference on

作者： Zihan Deng Zhisheng Wang Shanyuan Lin Guohang He Demin Jiang Shunli Wang Ultra Precision Optoelectronic Instrument Engineering Research Institute Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Engineering University Harbin China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

To reduce the harmful effects of radiation dose on the human body, the sparse scanning method is widely used. Recently, deep learning methods have been widely used in rather challenging reconstruction tasks such as sparse angles, and the dual domain method has a natural advantage because it can process the information in both the sine domain and the image domain. However, the existing two-domain methods do not pay enough attention to the allocation of training costs and strategies for the two domains. To solve this problem, in this paper, we design a Computation Overhead Optimization Dual-Domain Network with Multi-Level Cross-Domain Connections for Sparse View CT Reconstruction. We carries out feature extraction and image recovery for projection and image respectively through two Transformer-based subnets of coding-decoding type. In particular, in the two domains, we use the multilevel cross-domain connection method to connect the coding layers of the same scale in the two subnetworks, and adopt the two-channel method for up-sampling .The experimental results on clinical data validate the effectiveness of our method and training strategy. From the subjective and objective evaluation indexes of reconstruction results, our method can outperform some of the most advanced methods in the past.

关键词： Training Deep learning Computed tomography Signal processing Transformers Resource management Speech processing Positron emission tomography image reconstruction Optimization

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