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CA-GAN: Object Placement via Coalescing Attention based Generative Adversarial Network

CA-GAN: Object Placement via Coalescing Attention based Gene...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Wang, Yibin Feng, Yuchao Wu, Jie Xu, Honghui Zheng, Jianwei Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665468916

Learning to posit a foreground object over a background scene is an intriguing yet challenging problem, which frequently emerges in applications such as image editing and scene parsing. To date, most existing studies are fed up with knotty issues, including the deficiency of harnessing the interaction between the object and the scene, the astriction of involving little prior knowledge during training, etc. To break the shackles, we propose a novel end-to-end framework dubbed Coalescing Attention based Generative Adversarial Network (CA-GAN). Specifically, in our synthesizer, a feature polymerizer is designed to distill multi-scale information from both background and foreground. On that basis, a dual-branch coalescing attention module is proposed for a better exploration of the global feature-interaction relationships between object and scene. In addition, we add a supervised trail to learn the prior knowledge from the positive composite image, which further guides the synthesizer to discover a credible placement for the foreground object. With extensive experiments conducted on the OPA dataset, our proposal presents superiority in both rationality and diversity compared with other state-of-the-art methods. Our code is available at https://***/ZhengJianwei2/CA-GAN.

关键词： object placement image compositing coalescing attention generative adversarial network

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Decoupling While Coupling: Towards More Accurate Stereo image Sand Removal Beyond Certainty

Decoupling While Coupling: Towards More Accurate Stereo Imag...

引用

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

作者： Wei, Bingcai Liu, Hui Qian, Chuang Jia, Yi Wu, Wangyu Li, Zhishan School of Computer Science Wuhan University Wuhan China GNSS Research Center Wuhan Unicersity Wuhan China Intelligent Transport Systems Research Center Wuhan University of Technology Wuhan China College of Information and Control Engineering Jilin Institute of Chemical Technology Jilin China University of Liverpool Liverpool United Kingdom School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

Stereo image sand removal is crucial to improve the perceptual quality for autonomous driving perception. Existing methods often fall short in accurately estimating the uncertainty inherent in degraded images, leading to suboptimal outcomes. To address this, we introduce a novel framework named Decoupling While Coupling(DWC). DWC pioneers the integration of inter-view uncertainty estimation, cross-view uncertainty-aware interaction and block-wise uncertainty representation for superior stereo image sand removal. For cross-view information interaction, we propose an Uncertainty-aware Cross-view Attentive Interaction module(UCAI) to cope with the lack of uncertainty estimation ability in the existing cross-view information interaction mechanism. For the uncertainty perception and information interaction within the inter-view, we propose a Distribution Modeling Coupling Block(DMCB), which transmits the representation of uncertainty between each backbone module. For block-wise uncertainty estimation, we use our proposed Uncertainty-aware Distribution Feature Modulator(UDFM) as the backbone of DWC to modulate the uncertainty inside the neural network itself. Extensive experimental validations on our proposed stereo image sand removal dataset SandST confirm the efficacy of DWC. Our method not only achieves higher PSNR and SSIM, but also exhibits enhanced robustness against various sand degrees and patterns. © 2025 ieee.

关键词： Cross-view Inter-view Sand removal Stereo image Uncertainty estimation

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MICE - Medical image Contrast Enhancement 3

MICE - Medical Image Contrast Enhancement

引用

3rd ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2023

作者： Chen, Zi-Yu Wang, Lei Department of Electrical Engineering Feng Chia University Taichung City Taiwan

ISBN: (纸本)9798350333862

Medical images are essential in pathological diagnosis, especially for tumors, osteoporosis, and cerebral hemorrhage. In addition to general medical diagnosis, medical images are used to guide devices during surgery. Most medical images produced by imaging equipment are in black and white. Due to equipment performance and environmental limitations, these images are unclear and dim. In most medical images, medical personnel needs to observe a certain lesion clearly in the region of interest (ROI), when other regions have relatively little impact on diagnosis. Therefore, we propose a Medical image Contrast Enhancement (MICE) technology to identify the ROI and perform special enhancement on it. The area outside the ROI is compressed as much as possible after evaluation so that the ROI area has more visible space but the details of the non- ROI area are not discarded. This method allows medical staff to select a position in the area to confirm the object to be observed. Then, the grayscale data is analyzed to find out the grayscale area of ROI, stretch the grayscale, distinguish the adjacent brightness, and highlight the desired object. Observation details are used for the organ or tissue to be diagnosed. To verify the effectiveness of this method, we compare MICE with the CLAHE technology that is often used and the representative technology AGCWD of the AGC image contrast enhancement method, and the LIME image enhancement technology that is currently receiving attention. The regional entropy, regional standard deviation (Std), regional signal-to-noise ratio (SNR), and medical image evaluation indicators are measured to prove that the image processed by MICE has an excellent enhancement effect and distortion-free display. © 2023 ieee.

关键词： Diagnosis

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BlurPaint: image Inpainting using Blurring Diffusion Models

BlurPaint: Image Inpainting using Blurring Diffusion Models

引用

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

作者： Chen, Linxu Guo, Zhiqing Wang, Liejun Lu, Ke School of Computer Science and Technology Xinjiang University Urumqi China Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center Urumqi China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

The denoising diffusion probabilistic model (DDPM) for image inpainting transforms the input image to Gaussian noise by adding noise in the forward process, and reconstitution masked regions that are consistent with unmasked regions from pure noise in the reverse process. Recently, on inpainting methods based on DDPM, the unmasked regions failed to completely degrade into the same pure noise as the masked regions in the forward process, while in the reverse process, the unmasked and masked regions began to denoise from the pure noise at the same time, which led to the inconsistency between the forward and reverse processes. To this end, we propose BlurPaint, which completely degrades unmasked regions in the forward process and keeps consistent with masked regions that become Gaussian noise. In the reverse process, the unmasked regions are combined to complete the image. To better extract useful information from pure noise, a multi-scale feature fusion module (MFFM) is designed as the component of the denoise network, which learns local and non-local features through multi-scale receptive fields. Experimental results on multiple benchmark datasets demonstrate the performance of the proposed model. Our code is available at https://***/vpsg-research/BlurPaint. © 2025 ieee.

关键词： diffusion model image inpainting multi-scale receptive fields

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Haptic Dataset Augmentation with Subjective QoE Labels using Conditional Generative Adversarial Network

Haptic Dataset Augmentation with Subjective QoE Labels using...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Wang, Zican Xu, Xiao Yang, Dong Wang, Zhenyu Shtaierman, Sarah Steinbach, Eckehard Tech Univ Munich Munich Inst Robot & Machine Intelligence MIRMI Sch Computat Informat & Technol Dept Comp EngnChair Media Technol Munich Germany Tech Univ Dresden Ctr Tactile Internet Human In The Loop CeTI Dresden Germany

ISBN: (纸本)9781665491907

This paper proposes a novel Generative Adversarial Network (GAN)-based strategy to augment subjective haptic Quality of Experience (QoE) datasets for bilateral teleoperation with haptic feedback without conducting time-consuming subjective experiments. In our previous work, we proposed a multi-assessment fusion approach to predict subjective haptic quality using a collection of objective metrics. This method requires a sufficiently large haptic dataset with QoE labels. The proposed generative approach automatically expands the existing haptic quality dataset by combining a modified conditional GAN (CGAN) and Style GAN (StyleGAN) architecture. The most important feature of our method is that it learns from the labeled training data and focuses on synthesizing signals with artifacts according to new input labels containing the QoE score, time delay, control method, and data reduction information. Extensive experiments are conducted to validate the suitability of the expanded dataset. The results show that our approach is able to generate new data, which match the label and signal distribution of the original data with categorical rank and linear correlation of over 0.85.

关键词： Generative adversarial networks

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Sandformer: CNN and Transformer under Gated Fusion for Sand Dust image Restoration 48

Sandformer: CNN and Transformer under Gated Fusion for Sand ...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Shi, Jun Wei, Bingcai Zhou, Gang Zhang, Liye Xinjiang University School of Information Science and Engineering China Shandong University of Technology College of Computer Science and Technology China

ISBN: (纸本)9781728163277

Although Convolutional Neural Networks (CNN) have made good progress in image restoration, the intrinsic equivalence and locality of convolutions still constrain further improvements in image quality. Recent vision transformer and selfattention have achieved promising results on various computer vision tasks. However, directly utilizing Transformer for image restoration is a challenging task. In this paper, we introduce an effective hybrid architecture for sand image restoration tasks, which leverages local features from CNN and long-range dependencies captured by transformer to improve the results further. We propose an efficient hybrid structure for sand dust image restoration to solve the feature inconsistency issue between Transformer and CNN. The framework complements each representation by modulating features from the CNN-based and Transformer-based branches rather than simply adding or concatenating features. Experiments demonstrate that SandFormer achieves significant performance improvements in synthetic and real dust scenes compared to previous sand image restoration methods. © 2023 ieee.

关键词： CNN branch Gate fusion Sand image restoration Transformer branch

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A Review of image Enhancement Methods based on Stochastic Resonance of Neuronal Networks 5

A Review of Image Enhancement Methods Based on Stochastic Re...

引用

5th ieee international conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Sun, Ximin Niu, Pingjuan Li, Yong Li, Xiaoming Zhao, Bingzhen Zhang, Bin Liu, Dan Sun, Bo State Gride Ecommerce Technology CO. LTD Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China Tiangong University Laboratory of Metaverse in Electric Power Industry Tian'jin China State Gride Ecommerce Technology CO. LTD Laboratory of Metaverse in Electric Power Industry Tian'jin China

ISBN: (纸本)9798350355925

Stochastic resonance has always been a research hotspot in the field of image processing. The ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. This behavior in the biological nervous system can be explained by the concept of 'stochastic resonance'. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. Then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected. © 2024 ieee.

关键词： Circuit resonance

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Study of image Forgery Detection using Scale Invariant Feature Transform 2

Study of Image Forgery Detection using Scale Invariant Featu...

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2nd international conference on Sustainable Computing and Data Communication systems, ICSCDS 2023

作者： Jyothi, V. Sri Priya Reddy, G. Sree Kavya, M. Harshitha Reddy, K. Shvithi Reddy, P. Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665491990

images are a crucial source of information and are also used as a legal references and proofs sometimes, but since the availability of it has made easy due to internet hence it is prone to many manipulations and tampering. image forging refers to the process of changing an original image in order to produce a false image for any fraudulent or other purpose. The most common form of forging is called copy-move forgery, and it consists of clipping or duplicating parts of the original picture and placing them elsewhere on the same image. Forgery detection techniques come in a variety of forms, including block-based and key-point based ones, just as there are various ways that forgeries are carried out. Through comparison with the original picture, the technique suggested in this article aids in identifying image forgeries. Using the Scale Invariant Feature Transform (SIFT) method, separate the invariant features from the original and modified images. The advantages of SIFT over other algorithms is that it helps in reducing computational expenses, increases accuracy, and provides better results. © 2023 ieee.

关键词： image Forgery Scale invariant feature transform tampering

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Residual Hybrid Attention Network for Compression Artifact Reduction 48

Residual Hybrid Attention Network for Compression Artifact R...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Luo, Bingchun Yu, Wei Harbin Institute of Technology School of Computer Science and Technology Weihai China

ISBN: (纸本)9781728163277

Residual Hybrid Attention Network (RHAN) can restore images of arbitrary compression quality through flexibly fusing high-frequency features in the spatial and frequency domains based on the input quality factor. Specifically, to remove the compression artifacts, we propose a hybrid attention block (HAB) to adaptively restore the loss of high-frequency components, which parallelly predicts attention maps along two separate dimensions spatial and frequency spectra. To recover the compressed image flexibly and controllably, we further design a modulation decompression block (MDB), which employs a prior factor to learn a pair of modulation parameters and performs adaptively affine transformation on the obtained high-frequency features, thereby achieving high-quality image restoration at arbitrary compression levels. The quantitative and qualitative experiments on various public data sets show that the RHAN achieves the best performance and optimal visual perceptual quality in the JPEG image restoration with arbitrary compression levels. © 2023 ieee.

关键词： Compression artifacts reduction image restoration Multi-spectral attention mechanism

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StGAN: A Novel Symbolic signal Decomposition Base on GANs and Swin Transformer

StGAN: A Novel Symbolic Signal Decomposition Base on GANs an...

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2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Sun, Ming Guo, Li Chen, Long The College of Computer Science & Technology Qingdao University Qingdao China University of Macau Faculty of Science and Technology Macau China

ISBN: (纸本)9798350337020

The symbolic imagery signal decomposition is a common problem in digital signal processing. Its main purpose is to divide the symbolic imagery signal into different parts. However, in real-world applications, symbolic imagery signal captured by the camera is usually influenced by complex negative lighting environments such as highlights, reflections, drop shadows, or information loss. To overcome those problems, a generative adversarial network with Swin Transformer (StGAN) is proposed and applied to symbolic imagery signal decomposition and semantic segmentation tasks. In addition, a realistic image dataset taken in complex lighting conditions is proposed for symbolic imagery signal decomposition, which have complex lighting environments and edge information loss, we name it the MLT dataset. We demonstrate StGAN brings significant improvements in performance than some existing methods in the accuracy of symbolic imagery signal decomposition on MLT datasets. Further experiments on Sky and Facades datasets prove that StGAN works well in other tasks, especially when fewer data is involved in training, but it can still ensure good accuracy. © 2023 ieee.

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