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Enhanced Infrared-Visible image Fusion Method with an ISP Based Degradation Model 13

Enhanced Infrared-Visible Image Fusion Method with an ISP Ba...

引用

13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Guo, Xueyi Liu, Yisha Xue, Weimin Zhang, Zhiwei Information Science and Technology College Dalian Maritime University Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

The performance of infrared (IR) and visible (VIS) image fusion tasks depends on the quality of source images, which are usually impacted by various degradation factors in real-world scenarios, leading to poor quality and visual effects of the fused images. By learning the mapping from low-quality (LQ) source images to high-quality (HQ) fused images, neural networks can remove the impacts of degradation factors, generating HQ fused images. However, the lack of paired LQ source images and corresponding HQ fused images poses a significant challenge in training a fusion network. Considering the effectiveness of the image processing pipeline (ISP) in processing IR and VIS images, reversing specific steps of ISP can accurately simulate the image degradation process. Building upon this research, we propose a novel ISP-based degradation model that reverses several ISP steps while incorporating various degradation factors, thereby generating LQ IR and VIS images from source images to train the fusion network. Experimental results on multiple image fusion datasets demonstrate that our method significantly enhances the quality and visual effects of fused images. © 2025 ieee.

关键词： image fusion

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Lossless Phase Conversion Method for Object Wave-based Hologram Compression

Lossless Phase Conversion Method for Object Wave-based Holog...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Kojima, Hiroki Koiso, Ryota Watanabe, Ryosuke Nonaka, Keisuke KDDI Research Inc. Japan

ISBN: (纸本)9798350368741

An object wave is a suitable intermediate representation format for transmitting holographic application data because it is compatible with various playback devices. However, compressing the phase component of the object wave presents challenges due to its unique signal characteristics, i.e., periodicity and randomness. Hence, object waves significantly differ from ordinary 2D images such as camera-captured and screen pictures. To overcome the challenges, we propose a conversion method for lossless phase image coding using differential pulse-code modulation. Specifically, we calculate phase differences using adjacent pixels in two directions, considering periodic cycles of 2π, and then input them into the existing image coding. This method ensures robustness against randomness by smoothing the phase difference using the average of adjacent pixels. Experimental results show that lossless phase coding with the proposed conversion method achieves coding efficiency improvements of 6.50 % over lossless phase coding without the proposed method. © 2025 ieee.

关键词： Computer-generated hologram Differential pulse-code modulation Lossless image coding Object wave data

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8D Hyperchaotic System and Gold Sequences for Improved Medical image Cube Encryption 6

8D Hyperchaotic System and Gold Sequences for Improved Medic...

引用

6th ieee International conference on image processing, Applications and Systems, IPAS 2025

作者： Mamdouh, Eyad Gabr, Mohamed Megalli, Youstina Alexan, Wassim German University in Cairo Physics Department Cairo Egypt German University in Cairo Faculty of MET CSEN Department Cairo Egypt German University in Cairo Faculty of IET Communications Department Cairo Egypt

ISBN: (纸本)9798331506520

This paper presents an advanced encryption algorithm specifically designed to enhance the security of volumetric medical image data, crucial for the Internet of Medical Things (IoMT). The algorithm encrypts a stack of 256 images, each with dimensions of 256×256 pixels, through a meticulous multi-stage process. It begins by segmenting an image cube into Red, Green, and Blue channels, which are then encrypted through three phases: XOR operations with keys from an 8D hyperchaotic system, substitution with S-boxes derived from Gold sequences, and a final transformation using Fibonacci Q-matrices. This approach significantly improves security by improving entropy, decreasing cross-correlation, and strengthening resistance to statistical attacks, with various cryptographic seeds used at each stage to enhance the robustness of the encryption. Specific performance evaluation metrics include a pixel cross-correlation of approximately 0, an NPCR of 99.61%, a UACI of 34.45%, an entropy of 7.998, and a key space that exceeds 22870. Extensive cryptographic assessments confirm the effectiveness of this algorithm, making it a vital tool for securing medical image data within IoMT, ensuring safe transmission and storage in healthcare systems. © 2025 ieee.

关键词： Q factor measurement

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An Investigation on LLMs' visual Understanding Ability Using SVG for image-Text Bridging

An Investigation on LLMs' Visual Understanding Ability Using...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Cai, Mu Huang, Zeyi Li, Yuheng Ojha, Utkarsh Wang, Haohan Lee, Yong Jae University of Wisconsin-Madison United States University of Illinois Urbana-Champaign United States

ISBN: (纸本)9798331510831

Large language models (LLMs) have made significant advancements in natural language understanding. However, through that enormous semantic representation that the LLM has learnt, is it somehow possible for it to understand images as well? This work investigates this question. To enable the LLM to process images, we convert them into a representation given by Scalable Vector Graphics (SVG). To study what the LLM can do with this XML-based textual description of images, we test the LLM on three broad computer vision tasks: (i) visual reasoning and question answering, (ii) image classification under distribution shift, few-shot learning, and (iii) generating new images using visual prompting. Even though we do not naturally associate LLMs with any visual understanding capabilities, our results indicate that the LLM can often do a decent job in many of these tasks, potentially opening new avenues for research into LLMs' ability to understand image data. Our code, data, and models can be found here https://***/mu-cai/svg-11m. © 2025 ieee.

关键词： Scalable video coding

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ALIC: Adaptive Fusion Entropy Model for Learned image Compression

ALIC: Adaptive Fusion Entropy Model for Learned Image Compre...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Lingxue Liu, Meiqin Zhang, Yifan Tang, Qi Yao, Chao Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing China Visual Intelligence + X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9798350368741

Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned image compression (ALIC). To explore the correlation between channel and global spatial features, an adaptive fusion entropy model (AFEM) is designed. AFEM first slices the latent representation along the channels and leverages the adaptive channel fusion context module (ACFC) to capture correlations between the decoded and current slices. Subsequently, AFEM uses the adaptive spatial fusion context module (ASFC) to further divide the current slice into encoding pixels and reference pixels, thus improving the accuracy of probability estimation. The attention map and modulation parameter are introduced in ACFC and ASFC to interact with channel and spatial features. In addition, the variable-rate residual transformer (VResFormer) is proposed to control dynamic bit-rate by selectively modulating the high-frequency component according to coefficient weight and bias. Experimental results indicate that our ALIC outperforms other learned image compression algorithms. Our ALIC saves 5.89% bit-rate compared with VVC (4:4:4) on Kodak dataset. © 2025 ieee.

关键词： Entropy model Learned image compression Variable-rate

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Underwater image Restoration Based on Improved SwinIR Model 13

Underwater Image Restoration Based on Improved SwinIR Model

引用

13th International conference on Intelligent Control and Information processing, ICICIP 2025

作者： Lu, Yanke Li, Fang Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinIR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed;while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands. © 2025 ieee.

关键词： Convolution

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Joint Dehazing and Low-light image Enhancement with Complete Atmospheric Model 16

Joint Dehazing and Low-light Image Enhancement with Complete...

引用

16th International conference on Signal processing Systems, ICSPS 2024

作者： Wu, Yong Xia, Kexiang Ge, Liyong Ye, Jiongkai Zhejiang Institute of Communications Hangzhou311112 China The School of Automation Hangzhou Dianzi University Hangzhou310000 China

ISBN: (纸本)9781510689251

Current image dehazing algorithms often encounter issues of contrast reduction and color distortion in the shadow regions of images. To address this challenge, this paper proposes a comprehensive atmospheric model that simultaneously considers haze and low-light conditions. This model incorporates a global darkness factor as a disturbance variable alongside the atmospheric scattering model, enabling adaptive luminance balancing without the need to estimate the global atmospheric light or the global darkness factor. Building on the principle of object color rendition, This paper justifies the use of the dark channel prior for haze removal and extends the bright channel prior for low-light image enhancement. Furthermore, we explore the mapping relationship between normal and inverted images based on the complete atmospheric model. By solving for the transmission rates of the global atmospheric light and global darkness factor, we propose an image enhancement algorithm capable of concurrently processing haze and low-light images. The proposed algorithm effectively mitigates issues of contrast reduction and color distortion. Experimental results demonstrate the efficacy of our approach, showing that the visual outcomes of simultaneous haze removal and low-light enhancement surpass those achieved through single-task haze removal. © 2025 SPIE.

关键词： Demulsification

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Application of Various Novel Computer image processing (CIP) Algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 ieee.

关键词： image compression

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CoF: Coarse to Fine-Grained image Understanding for Multi-modal Large Language Models

CoF: Coarse to Fine-Grained Image Understanding for Multi-mo...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Yeyuan Gao, Dehong Li, Bin Long, Rujiao Yi, Lei Cai, Xiaoyan Yang, Libin Zhang, Jinxia Yu, Shanqing Xuan, Qi School of Automation Northwestern Polytechnical University Shaanxi Xi'an China School of Cybersecurity Northwestern Polytechnical University Shaanxi Xi'an China Alibaba Group Zhejiang Hangzhou China The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China Advanced Ocean Institute Southeast University Nantong226010 China Zhejiang University of Technology Zhejiang Hangzhou China Binjiang Institute of Artificial Intelligence Zhejiang Hangzhou China

ISBN: (纸本)9798350368741

The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks. However, current MLLM often struggles to effectively address fine-grained multi-modal challenges. We argue that this limitation is closely linked to the models' visual grounding capabilities. The restricted spatial awareness and perceptual acuity of visual encoders frequently lead to interference from irrelevant background information in images, causing the models to overlook subtle but crucial details. As a result, achieving fine-grained regional visual comprehension becomes difficult. In this paper, we break down multi-modal understanding into two stages, from Coarse to Fine (CoF). In the first stage, we prompt the MLLM to locate the approximate area of the answer. In the second stage, we further enhance the model's focus on relevant areas within the image through visual prompt engineering, adjusting attention weights of pertinent regions. This, in turn, improves both visual grounding and overall performance in downstream tasks. Our experiments show that this approach significantly boosts the performance of baseline models, demonstrating notable generalization and effectiveness. Our CoF approach is available online at https://***/Gavin001201/CoF. © 2025 ieee.

关键词： fine-grained understanding prompt engineering vision and language

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Spatial-Frequency Information Interaction Diffusion for SAR Colorization

Spatial-Frequency Information Interaction Diffusion for SAR ...

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2025 ieee International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Xupei Qin, Hanlin Li, Jingjing Geng, Jinni Gao, Zihan Yu, Yue School of Optoelectronic Engineering Xidian University Xi'an710071 China Xi'an Key Laboratory of Infrared Technology and System Xi'an710071 China

ISBN: (纸本)9798350368741

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available. © 2025 ieee.

关键词： Diffusion Model image colorization Spatial-Frequency Interaction Synthetic aperture radar

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