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Dual-Teacher Color and Structure Distillation for NIR image Colorization

Dual-Teacher Color and Structure Distillation for NIR Image ...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Park, Tae-Sung Kim, Jong-Ok Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350359855

Near-infrared (NIR) imaging can acquire more details and textures with less noise in low-light environments compared to RGB. As a result, it has been widely used in low-light vision scenarios such as CCTV, autonomous driving, and military applications. However, applying NIR images to the human cognitive system and computer vision algorithms is challenging due to the lack of color information. Therefore, it is essential to colorize NIR images into RGB ones. We propose a teacher-student deep network with dual-Teacher knowledge distillation to better estimate the original color and structure information. Specifically, our dual-Teacher network is designed to separately teach distinct knowledge, such as color and structure qualities in an image, to the student. Finally, a color guided structure (CGS) and color embedding (CE) module are applied to fuse color and structure features. Our trained model can retain color consistency and detailed structure information of objects. © 2023 ieee.

关键词： Color

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DepthUNet: A Dehazing Model with Adaptive Depth Attention for Natural images

DepthUNet: A Dehazing Model with Adaptive Depth Attention fo...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Wang, Kai-Yu Lin, Yu-Hsiang Hsiung, Pao-Ann National Chung Cheng University Department of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350359855

Most image dehazing deep learning models target synthetic datasets of hazy images, resulting in not considering features in natural hazy images. Leveraging on depth attention with adaptation, we propose a novel dehazing network called depthUNet, that is focused on natural images. Utilizing the correlation between depth information and haze distribution, our network enhances its generalization performance on natural images. Furthermore, our method improves the PSNR for the non-homogeneous realistic haze dataset NH-HAZE from 20.66 (DeHamer's result) to 20.74, using only 1.6% of the parameters. Similarly, for the outdoor scenes realistic haze dataset O-HAZE, our method enhances the PSNR from 24.36 (MSBDN's result) to 25.77, with just 6% of the parameters. On natural road images with haze from dataset RTTS, our method improved the vehicle detection rate by 10% in terms of the R-squared value. In summary, our method outperforms all state-of-The-Art methods while utilizing the least number of parameters. © 2023 ieee.

关键词： image enhancement

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Multi-Contrast MR image Synthesis with a Brownian Diffusion Model 32

Multi-Contrast MR Image Synthesis with a Brownian Diffusion ...

引用

32nd ieee Signal processing and communications Applications conference (SIU)

作者： Kabas, Bilal Arslan, Fuat Nezhad, Valiyeh Ansarian Cukur, Tolga Bilkent Univ Elekt Elekt Muhendisligi Bolumu Ankara Turkiye Bilkent Univ Ulusal Manyetik Rezonans Arastirma Merkezi UMRAM Ankara Turkiye Bilkent Univ Sinirbilim Program Muhendislik & Fen Bilimleri Enstitusu Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

Magnetic Resonance Imaging (MRI) plays a significant role in medical diagnostics. However, prolonged scan times may hinder its widespread applicability in clinical settings. To mitigate this challenge, certain contrasts within multi-contrast MRI protocols can be excluded, and these target contrasts can then be synthesized from the acquired set of source contrasts retrospectively. Recently introduced generative adversarial and diffusion based MRI synthesis models yield enhanced performance against classical methods, yet there can still benefit from technical improvements. In this study, we propose a Brownian diffusion-based multi-contrast MR image synthesis model. Existing diffusion models synthesize images starting from a Gaussian noise sample, so guidance from the source contrast images are weakened. Conditional denoising diffusion models employs a weak conditioning during reverse process within the denoising network that may result in suboptimal sample generation due to poor convergence to target distribution. Capitalizing Brownian diffusion, the proposed model instead incorporates stronger guidance toward the target contrast distribution via a refined diffusion process. Experimental results suggest that our method attains higher performance in noise reduction and capture of tissue structural details over existing methods.

关键词： MRI synthesis contrast diffusion deep learning

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High-frequency guided CNN for video compression artifacts reduction

High-frequency guided CNN for video compression artifacts re...

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ieee International conference on visual communications and image processing (VCIP)

作者： Yu, Li Chang, Wenshuai Liu, Qingshan Gabbouj, Moncef Nanjing Univ Informat Sci & Technol Sch Comp Sci Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing Peoples R China Tampere Univ Dept Comp Sci Tampere Finland

ISBN: (纸本)9781665475921

In this paper, we propose a high-frequency guided CNN for video compression artifacts reduction. In the proposed method, high frequency component in Y channel is extracted and used to guide the quality enhancement of all Y, U, V channels. As high frequency component contains the edge and contour information of the objects in the image, which is of vital importance to both subjective and objective quality. In general, the proposed method consists of two modules: the high frequency guidance module and the quality enhancement module. The high-frequency guidance module uses multiple octave convolutions to extract the high-frequency component in Y channel and then fuse it into the features of Y, U, and V channels. While in the quality enhancement module, multiple CNN residual blocks are used for the quality enhancement of Y, U, and V channels. The proposed method was integrated into both HM-16.22 and VTM-16.0. The results on the JVET test sequence under All Intra configuration shows the effectiveness of the proposed method. Compared with HEVC, the proposed method achieves the average BD-rate reductions of -12.3%, -22.7% and -23.5% for Y, U and V channels respectively. Compared with VVC, the average BD-rate reductions are -6.7%, -12.3% and -13.2% correspondingly.

关键词： Deep Learning CNN High Frequency Video Coding Post-processing

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Infrared and Visible image Fusion with Hierarchical Human Perception

Infrared and Visible Image Fusion with Hierarchical Human Pe...

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

作者： Yang, Guang Li, Jie Liu, Xin Zhong, Zhusi Gao, Xinbo School of Electronic Engineering Xidian University Xi'an China Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350368741

image fusion combines images from multiple domains into one image, containing complementary information from source domains. Existing methods take pixel intensity, texture and high-level vision task information as the standards to determine preservation of information, lacking enhancement for human perception. We introduce an image fusion method, Hierarchical Perception Fusion (HPFusion), which leverages Large Vision-Language Model to incorporate hierarchical human semantic priors, preserving complementary information that satisfies human visual system. We propose multiple questions that humans focus on when viewing an image pair, and answers are generated via the Large Vision-Language Model according to images. The texts of answers are encoded into the fusion network, and the optimization also aims to guide the human semantic distribution of the fused image more similarly to source images, exploring complementary information within the human perception domain. Extensive experiments demonstrate our HPFusoin can achieve high-quality fusion results both for information preservation and human visual enhancement. Our code is available at https://***/SSyangguang/HPFusion. © 2025 ieee.

关键词： Human Perception image Fusion Large Vision-Language Model

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image Forgery Detection Using Hybrid Model

Image Forgery Detection Using Hybrid Model

引用

2024 ieee International conference on Intelligent Signal processing and Effective Communication Technologies, INSPECT 2024

作者： Metri, Priya Balla, Sakshi Pawar, Shreya Sharma, Atharva Sakunde, Prashant Dr. D. Y. Patil Institute of Technology Artificial Intelligence and Data Science Pimpri Pune India

ISBN: (纸本)9798350379525

The growing demand for trustworthy picture forgery detection techniques to maintain the integrity of visual content across a range of applications is addressed in this work. To improve picture authentication accuracy and efficiency, a user-friendly cascaded framework combining Support Vector Machines (SVM) and VGG 16 is presented. First, a highly effective supervised learning system called Support Vector Machines (SVM) rapidly filters photos by identifying overt alteration indi-cators. After passing this phase, images are examined and refined even more with the help of the VGG 16 deep learning model. When SVM and VGG16 are combined, their complimentary strengths are utilised, which improves accuracy and speeds up identification when compared to other approaches. According to benchmark datasets, the proposed method outperforms earlier methods with respect to accuracy and computational efficiency. This strategy may benefit many real-world applications- based on forensic verification, verification of media integrity, medical image authentication, and intellectual property protection-in the here-and-now solutions. This work enhances the confidence and trustworthiness of digital communications by providing an efficient, friendly user tool for the verification of visual content. © 2024 ieee.

关键词： Support vector machines

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Game Character Generation with Generative Adversarial Networks 30

Game Character Generation with Generative Adversarial Networ...

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30th ieee Signal processing and communications Applications conference (SIU)

作者： Emekligil, Ferda Gul Aydin Oksuz, Ilkay Istanbul Tech Univ Oyun & Etkilesim Teknol Bolumu Istanbul Turkey Istanbul Tech Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781665450928

Designing visual content and characters for games is a time consuming task even for designers and illustrators with experience. Most of the game companies and developers use procedural methods to automate the design process. The visual content produced by these algorithms is limited in terms of variation. In this paper, we propose to use Generative Adversarial Networks (GANs) for visual content production. Two different rpg and dnd visual image datasets were collected over the internet for training and 6 different GAN models were trained on them. In 3 of 18 experiments, transfer learning methods are used because of the limited datasets. The Frechet Inception Distance metric was used to compare the model results. As a result, SNGAN was the most successful in both datasets. Moreover, the transfer learning method (WGAN-GP, BigGAN) was more successful than the from scratch method.

关键词： Generative Adversarial Network Generative Learning image Generation Game Character Generation

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DSPI - Dual Semantic Parsing image: A Robust Person Representation for Person Re-Identification 10

DSPI - Dual Semantic Parsing Image: A Robust Person Represen...

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10th ieee International conference on communications and Electronics (ieee ICCE)

作者： Dau Anh Dung Nakamura, Yasuhiro Natl Def Acad Grad Sch Sci & Engn Dept Informat & Math Sci Yokosuka Kanagawa Japan Natl Def Acad Sch Elect & Comp Engn Dept Comp Sci Yokosuka Kanagawa Japan

ISBN: (纸本)9798350379808;9798350379792

One method to significantly enhance the performance of Person-ReID models is to leverage segmentation models to extract semantic parsing information, thus emphasizing the characteristics of human body parts. Studies employing this approach typically utilize CNN architectures as the backbone of Person-ReID, integrating human semantic parsing information into the output of the backbone model, resulting in substantial performance improvement. However, Transformer architectures, utilizing self-attention mechanisms and employing different image data processing techniques from CNN architectures, necessitate a direct integration method for human semantic parsing information into the model's input. In this study, we propose a novel, simple, and highly adaptable method for integrating human semantic parsing information into input data, called Dual Semantic Parsing image (DSPI). In this method, a pre-trained segmentation model is employed to extract human pixels from the background and create a new foreground image. The DSPI is formed by horizontally merging the foreground image with the original image, which serves as the input image for the Re-ID model. Our study demonstrates that the Person Re-ID Backbone model ViT+DSPI achieves 93.1% Rank-1 accuracy and 91.1% mAP in the same-domain test of DukeMTMC-ReID, thus attaining state-of-the-art performance. Additionally, significant performance enhancements were observed in cross-domain tests.

关键词： Person Re-identification Semantic parsing Dual Semantic Parsing image (DSPI)

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Online Learning of image Recognition Task Offloading Policies over Wireless Links

Online Learning of Image Recognition Task Offloading Policie...

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ieee International conference on communications (ieee ICC)

作者： Koutsopoulos, Iordanis Athens Univ Econ & Business Dept Informat Athens Greece

ISBN: (纸本)9781538674628

We study the problem of online learning of optimal offloading policies for image processing tasks, for minimizing a cost that is weighted sum of transmit energy and object recognition error rate. A mobile node generates image processing tasks that involve object recognition. There exist three options: (i) transmit the image to a remote server for processing with a deep-learning (DL) model, (ii) process locally with a simpler model, (iii) apply a lightweight, error-prone technique for object detection, and if objects are detected, then send image to the server. The proper offloading decision requires knowledge of the transmit energy cost and object recognition error rate for each option. However, these processes are non-stationary due to unpredictable object occurrence, mobility and propagation dynamics, and they depend on the object inference result which is unknown at decision time. We cast the problem as an adversarial multi-armed bandit, in which the EXP3 algorithm achieves sublinear regret. For the constrained problem, we propose an algorithm that extends EXP3 and achieves good regret in the objective and constraint, thus asymptotically learning the optimal static randomized offloading policy, while satisfying the error constraint. Performance is validated via numerical experiments informed by real-life object recognition measurements and models.

关键词： Object recognition

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Accurate Segmentation of Brain Tumors in Magnetic Resonance images with Pyramid Stage Decomposition Network Approach 32

Accurate Segmentation of Brain Tumors in Magnetic Resonance ...

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32nd ieee Signal processing and communications Applications conference (SIU)

作者： Ari, Berna Gurler Uzen, Huseyin Sengur, Abdulkadir Inonu Univ Bilgisayar Muhendisligi Bolumu Malatya Turkiye Bingo Univ Bilgisayar Muhendisligi Bolumu Bingo Turkiye Firat Univ Elekt Elekt Teknol Bolumu Elazig Turkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978;9798350388961

This study explores the utilization of the Pyramid Scene Parsing Network (PSPNet) architecture to achieve accurate segmentation of brain tumors in magnetic resonance (MR) images. Experimental evaluations were conducted on different pre-trained backbone network models, including Vgg16, Inceptionv3, Mobilenetv2, Efficientnetb0, Resnet18, Resnet34, Resnet50, Resnet101, Resnext50, and Resnext101, assessing the performance of each model in brain tumor segmentation. The results highlight the VGG16-PSPNet model as the most successful, showcasing high F1-score, mIoU, precision, recall, and accuracy values.

关键词： Brain tumors Magnetic Resonance Imaging (MRI) Segmentation Pyramid Scene Parsing Network (PSPNet) Medical image Analysis

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