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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

引用

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

作者： Li, Baisong Wang, Xingwang Xu, Haixiao College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Krowledge Engineering Ministry of Education Jilin University China

ISBN: (纸本)9798350368741

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba. © 2025 ieee.

关键词： hyperspectral image Mamba spectral super-resolution state space model

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BIAWDiff: Enhancing Low-Light images with Bio-Inspired Attention and Wavelet Diffusion

BIAWDiff: Enhancing Low-Light Images with Bio-Inspired Atten...

引用

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

作者： Li, Zeyu Yang, Sheng Yang, Hanxiang Tang, Xiongxin Wu, Fengge Xu, Fanjiang Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggle with balancing brightness enhancement and detail preservation, leading to issues such as overexposure, artifacts, and loss of high-frequency details. To address these challenges, we propose a novel method, Bio-Inspired Attention and Wavelet Diffusion (BIAWDiff), that integrates Retinex theory with bio-inspired attention and wavelet-based diffusion models to enhance low-light images. BIAWDiff consists of three key modules: the Initial Light Restoration (ILR) module for brightness enhancement and noise reduction, the Rod Cell-Inspired Attention Refinement (RCAR) module for luminance refinement, and the Detail Refinement (DR) module for restoring high-frequency details. Experimental results demonstrate that BIAWDiff outperforms existing techniques, achieving superior results in brightness enhancement, noise reduction, and detail preservation, with an average PSNR increase of 5.1% and SSIM improvement of 3.2% on paired datasets, and a reduction in NIQE and BRISQUE by 7.4% and 8.6% on unpaired datasets. © 2025 ieee.

关键词： diffusion models Low-light image enhancement Retinex theory transformer wavelet domain

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Exploiting Foundation Models for Label-Efficient Few-Shot Learning via Feature Coupling: A Case Study of cardiac CT Segmentation

Exploiting Foundation Models for Label-Efficient Few-Shot Le...

引用

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

作者： Chen, Wei Li, Chen Zhou, Wenjuan Li, Yuhang Guo, Tianhang Tang, Yuhua College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

The scarcity of labeled data poses a significant challenge for deep learning-based medical image segmentation. To address this, this study introduces the novel Foundation Model-based Few-Shot Segmentation (FM-FSS) paradigm. FM-FSS capitalizes on the knowledge distilled from pre-trained foundation models, such as the Segment Anything Model, to enhance segmentation performance in few-shot scenarios. The paradigm designs a feature coupling module that synergizes SAM's powerful feature extraction capabilities with nnU-Net's self-configuration strategy, enabling accurate segmentation with minimal labeled data and optional manual prompt inputs. Extensive experiments on a publicly available cardiac CT dataset demonstrate that FM-FSS outperforms state-of-the-art segmentation models. With only 20 labeled images, our method achieves an average Dice score of 94.33% and an ASD of 1.10 mm. Moreover, FM-FSS maintains its label-efficient performance in a one-shot setup, reducing the annotation requirements by at least fourfold. The code and pre-trained models will be released upon acceptance. © 2025 ieee.

关键词： Few-shot learning Foundation model image segmentation SAM

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First-order State Space Model for Lightweight image Super-resolution

First-order State Space Model for Lightweight Image Super-re...

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

作者： Zhu, Yujie Zhang, Xinyi Lu, Yekai Yang, Guang Fang, Faming Zhang, Guixu School of Computer Science and Technology East China Normal University Shanghai China IT Department Guotai Junan Security Shanghai China

ISBN: (纸本)9798350368741

State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State Space Model (FSSM) to improve the original Mamba module, enhancing performance by incorporating token correlations. We apply a first-order hold condition in SSMs, derive the new discretized form, and analyzed cumulative error. Extensive experimental results demonstrate that FSSM improves the performance of MambaIR on five benchmark datasets without additionally increasing the number of parameters, and surpasses current lightweight SR methods, achieving state-of-the-art results. © 2025 ieee.

关键词： error analysis lightweight image super-resolution ODE state space model

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Simulation Verification Method for image Sensor Interface processing Unit Based on UVM 5

Simulation Verification Method for Image Sensor Interface Pr...

引用

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

作者： Ma, Wenji Beijing Sunwise Information Technology Ltd. Department of FPGA Verification Organization Beijing China

ISBN: (纸本)9798331533694

image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. The image Sensor Interface processing Unit is typically characterized by a high degree of complexity to support diverse image sensor interfaces. Consequently, the effective and efficient verification of this unit is essential for upholding project quality and adhering to timelines. This article presents a simulation verification methodology for the image Sensor Interface processing Unit utilizing the Universal Verification Methodology (UVM). It explores the features of different sensor interfaces, the architectural framework, core base classes, runtime mechanisms, and reusable components within the UVM verification platform. The study identifies functional verification points based on the specific characteristics and application contexts of the image Sensor Interface processing Unit and formulates testing incentives. By analyzing the simulation results, the approach ensures comprehensive coverage of all functional points, achieving a 100% functional coverage rate. The testing platform is systematically designed to provide robust portability while meeting fundamental functional verification requirements, thereby serving as a reference for the development of future module verification environments. © 2025 ieee.

关键词： Reusability

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A Novel Split Deep Unfolding Transformer for Pan-Sharpening

A Novel Split Deep Unfolding Transformer for Pan-Sharpening

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

作者： Chen, Jiannan Jiang, Zhizhuo Wang, Xueqian Li, Yaowen Wang, Huajie Liu, Yu Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Pan-sharpening is a commonly employed strategy to obtain high-resolution multispectral (HRMS) images. Existing deep unfolding networks for pan-sharpening suffer from ineffectively establishing the relationship between panchromatic (PAN) images and generated noisy HRMS (GN-HRMS) images in PAN-guided image denoising, lacking the support of physical models. In this paper, we first design a degradation-fusion-aware unfolding framework (DF-UF) to separate the processing of PAN-prior in PAN-guided image denoising into an individual module, PAN-prior processor, for better integrating physical models. Then, we derive a flexible intensity-hue-saturation (F-IHS) to act as the PAN-prior processor, which models the relationship between PAN images and GN-HRMS images in terms of intensity components through the intensity-hue-saturation (IHS) theory. Finally, plugging F-IHS into DF-UF, we propose a degradation-intensity-aware unfolding transformer (DIUT) to address the problem of incomplete utilization of PAN images in the denoising process. Extensive experiments on diverse scenes show that the performance of DIUT surpasses existing state-of-the-art methods. © 2025 ieee.

关键词： deep unfolding network intensity-hue-saturation pan-sharpening transformer

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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...

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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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Hyperspectral image Reconstruction with Unseen Material Detection

Hyperspectral Image Reconstruction with Unseen Material Dete...

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

作者： Ma, Ye Lin, Songnan Wen, Bihan School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Reconstruction of hyperspectral images (HSIs) from their RGB measurements is an ill-posed inverse problem. The key to successful reconstruction relies on establishing an effective HSI prior, for which deep learning techniques have achieved impressive performance. However, the scarcity of large-scale HSI datasets poses a significant challenge, limiting the practical application of deep HSI reconstruction methods and often leading to incorrect results when dealing with unseen substances or materials. To tackle this challenge, we propose a deep RGB-to-HSI reconstruction model based on the sparse prior of hyperspectral signals. The network can effectively correlate HSI and RGB features via shared sparse codes, representing the weights of spectral-unique materials. Besides, testing images with unseen materials can be detected by measuring their sparse modeling errors. Experimental results demonstrate that the proposed method achieves promising results on RGB-to-HSI reconstruction. Further, the sparse modeling error evidently demonstrates its efficacy as an indicator for unseen materials. © 2025 ieee.

关键词： hyperspectral reconstruction unseen material detection

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An Abnormal Audio Generation Method for Fault Diagnosis of Power Transformers

An Abnormal Audio Generation Method for Fault Diagnosis of P...

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

作者： Niu, Ben Wei, Yangjie Zhang, Ke Yu, Zhuoran Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China

ISBN: (纸本)9798350368741

Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model training, resulting in limited diagnostic performance. To address this problem, we propose an abnormal audio generation method based on the improved cycle generative adversarial networks (ImCycleGAN) to augment the limited training dataset. In the ImCycleGAN, the generator and discriminator are redesigned and adversarially trained to generate the realistic-like audios of six abnormal statuses. Moreover, we combine adversarial, cycle consistency and identity mapping losses to optimize the training process of ImCycleGAN and enhance its ability to capture nonlinear features of audios. Finally, the generated data is evaluated in terms of similarity and fault classification. Experimental results show that our method can generate abnormal audios with high similarity to the real ones, and significantly improve the classification accuracy of existing fault diagnosis models. © 2025 ieee.

关键词： abnormal audio generation fault diagnosis improved cycle generative adversarial networks

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HPDNET: Hyper Prior Dependent Demosaic Neural Network 6

HPDNET: Hyper Prior Dependent Demosaic Neural Network

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

作者： Seo, Youngil Jun, Sungho Lim, Dongpan Song, Seongwook Samsung Electronics System LSI Division Yongin Korea Republic of

ISBN: (纸本)9798331506520

Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are often characterized by heavy computational requirements, rendering them unsuitable for deployment on resource-constrained platforms. This presents a critical challenge in the field of image demosaicking: while deep learning approaches excel in enhancing image quality, their computational intensity poses a significant hindrance to their wider adoption. Consequently, there is a pressing need for methodologies that can strike an optimal balance between achieving superior image quality and maintaining computational efficiency. In this work, we propose a new deep framework, hyper-prior dependent demosaic neural network, HPDNet that utilizes the significant concepts of the conventional algorithm and the characteristic of image sensor data. We designed the network that exploits three concepts, those are pixel gradient prior attention, phase separation, and multi-level sparse and dense feature extraction. We designed the deep neural network that extracts the optimal gradient prior and the multi-level extracted features are fused and attended by gradient prior. It can fully utilize spatially variant information. Also to make the network deployed in the mobile platform, we devised self-pruned image convolution that adopts image filter characteristic and reduce computations. Experiments show that proposed network outperforms SOTA demosaic networks both in terms of image quality and computation. © 2025 ieee.

关键词： image quality

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