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Research on Real Scene image Super-Resolution Based on Diffusion Model 24

Research on Real Scene Image Super-Resolution Based on Diffu...

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yu Liu Xuan Li Yuxia Bie Shenyang Aerospace University Shenyang Liaoning China

ISBN: (纸本)9798400713880

Previous image super-resolution algorithms based on convolutional neural networks have primarily been trained on ideal synthetic datasets; however, their performance often declines significantly when applied to real-world scenarios. To effectively extract original information from real scene images, this paper proposes a generative diffusion model, Diffusion-Net, designed for real scene images, which integrates diffusion models into a U-shape network. Specifically, the backbone of this model consists of multiple layers of encoders and decoders, with processed images being fed into the diffusion model for further optimization. Additionally, we define a hybrid loss function to enhance the visual quality of the reconstructed images. To accelerate the model's training speed, a feature distillation module is designed. Furthermore, a multi-head attention mechanism is incorporated to enable the model to reconstruct high-quality texture details. The model is trained on the RealSR dataset, and experimental results demonstrate that, compared to state-of-the-art super-resolution models (such as RCAN and SwinIR), Diffusion-Net achieves superior performance across three key evaluation metrics—Peak Signal-to-Noise Ratio, Structural Similarity Index, and No-Reference image Quality Assessment—while also exhibiting enhanced visual effects.

关键词： Diffusion model

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Correcting Road image Annotations 20th

Correcting Road Image Annotations

引用

20th International conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Lam, Percy Chen, Weiwei de Silva, Lavindra Brilakis, Ioannis Civil Engineering Building University of Cambridge 7a JJ Thomson Avenue CambridgeCB3 0FA United Kingdom Bartlett School of Sustainable Construction University College London 1-19 Torrington Place LondonWC1E 7HB United Kingdom

ISBN: (纸本)9783031842078

Digitisation provides a promising avenue to meet new socio-economic demands in infrastructure delivery. The current state of practice suggests that human annotators still need to participate substantially in data preparation for digitising infrastructure, such as annotating real-life domain-specific visual datasets for road maintenance. Research in the past focuses on predicting better labels with less human effort, leaving a gap in not maximizing the gains from the subsequent human corrections of pseudo-labels to ground truths. The gap highlights the opportunities for a solution to mimic human corrections by "correcting like instances alike". We propose an extension to Mask R-CNN to tackle this gap. Our auto-correction method harvests learnings from past corrections and automates corrections in forthcoming images. The method first gauges the corrections made between the pseudo-labels and the final ground truth. The method then compares the feature vectors and attribute data of a new batch of unlabelled images against past corrections. When the deep learning model predicts similarly wrong features, the method will mimic human corrections in the past and prompt additions, deletions or category changes. The method concludes with post-processing to eliminate unwanted predictions. This similarity-based approach improves the precision of the testing batch by 15–70% and reduces the number of mouse clicks by approximately 20%. The solution therefore partially automates the human review after predicting pseudo-labels by similarity-based corrections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image annotation

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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Absolute visual Localization for Aerial Vehicles Based on Deep Learning image Matching 24

Absolute Visual Localization for Aerial Vehicles Based on De...

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yi Yang Linfeng Xu Boya Wang Yibin Han Dong Ye Harbin Institute of Technology Harbin Heilongjiang China

ISBN: (纸本)9798400713880

The range of applications for aerial vehicles is expanding rapidly, encompassing various fields such as transportation, security, rescue, and mapping. The foundation for the execution of these tasks is accurate localization. The prevailing localization method for aerial vehicles is currently reliant on the Global Navigation Satellite System (GNSS). However, this method is vulnerable to disruptions caused by occlusion or interference, particularly in environments such as mountainous regions, forests, and urban canyons. Inertial navigation systems and visual odometers are commonly employed in GNSS-denied environments; however, their long-term accuracy is poor due to error accumulation. Absolute visual localization, based on image matching, exhibits superior long-term accuracy and does not require external devices, making it a suitable alternative when GNSS is unavailable. Additionally, deep learning has demonstrated remarkable potential in image matching, offering higher accuracy compared to traditional methods. The proposed methodology utilizes a deep learning-based image matching technique, employing the LoFTR model to match features on rectified aerial images and segmented satellite maps. Addressing the sensitivity of the conventional regional perspective transformation to nonlinear image differences, a central perspective transformation is proposed to calculate the precise position of the aerial vehicle on the map. The experimental results, obtained from flights at an altitude of 500 m, demonstrate that the proposed method, with a spatial resolution of 1.1 m for the aerial images and 2.4 m for the map blocks, exhibits a mean absolute error of 17.39 m. This outcome signifies a 54.55% reduction in error when contrasted with the conventional approach on the same dataset, attaining a level of accuracy comparable to that of general GNSS localization. Furthermore, the proposed method exhibits robustness to seasonal alterations in ground scene, providing an accurate and

关键词： Deep learning

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Targeted image Manipulation 1st

Targeted Image Manipulation

引用

1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Sanjay Sen, S. Patel, Sameer Dushyant Bedict, A. Dilip, Golda Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9783031823824

This paper addresses the problem of customization of an image at an object-level, where the aim is to enable users to customize images containing objects unseen during training without explicit supervision. Our approach utilizes a self-supervised method coupled with a semantic embedding space to facilitate object-level manipulation without the need for paired data. Through extensive experiments on various datasets, we demonstrate the effectiveness of our method in accurately customizing images with unseen objects while preserving visual coherence. Our results showcase the potential of targeted image manipulation in diverse applications such as content creation and image editing. In conclusion, this work presents a promising avenue for enabling users to seamlessly customize images containing unseen objects, opening up new possibilities for creative expression and content manipulation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： content manipulation novel image customization object-level customization self-supervised method semantic embedding space

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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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Lung Cancer Diagnosis Using image Enhancement and Machine Learning Methods 6th

Lung Cancer Diagnosis Using Image Enhancement and Machine Le...

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6th International conference on Computational Intelligence in communications and Business Analytics, CICBA 2024

作者： Khan, Takreem Fatima Zubair, Swaleha Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)9783031813412

Lung cancer is one of the most prevalent cancers in the world and a leading cause of mortality. It causes due to the uncontrollable proliferation of malignant cells in the lungs. The likelihood of curing lung cancer can be increased by early detection and treatment of the disease in its early stages. As a result of this, practitioners find computer assisted diagnosis helpful in accurately identifying the cancerous cells. The integration of machine learning with image processing techniques has been heavily incorporated into numerous computer-aided methods, and they have proven to be extremely effective. The aim of this research is to outline a clear procedure for detecting nodules using image enhancement techniques on computed tomography images that were obtained from the LIDC IDRI dataset, and the use of machine learning algorithm to categorize those nodules into two classes: benign and malignant. Our suggested model stands out from other proposals since it focuses on early detection of cancer nodule and XGBoost outperforms other machine learning algorithm in classifying identified nodules with an accuracy of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Low Light image Enhancement Using Autoencoder-Based Deep Neural Networks 28th

Low Light Image Enhancement Using Autoencoder-Based Deep Neu...

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28th International conference on image processing, Computer Vision, and Pattern Recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Chen, Kuei-Yu Leou, Jin-Jang National Chung Cheng University Chiayi621 Taiwan

ISBN: (纸本)9783031859328

In this study, a low light image enhancement approach using autoencoder-based deep neural networks is proposed. The proposed approach consists of two subnets, namely, feature extraction (FE) subnet and color enhancement (CE) subnet. FE subnet extracts image features from the low light image and enhances detailed textures, while CE subnet recovers the color information of the low light image and performs image denoising. A pre-processing technique, namely, relative global histogram stretching (RGHS) is employed in CE subnet. Finally, the processing results of FE and CE subnets are fused to generate the final enhanced image. Based on the experimental results obtained in this study, in terms of two objective performance metrics (PSNR (dB) and SSIM) and subjective evaluation, the performance of the proposed approach is better than those of five comparison approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Implementation of a Comparative Study of Convolutional Neural Network Architectures for image Blind Noise Elimination 4th

Implementation of a Comparative Study of Convolutional Neura...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Anzal, Oumaima Guessous, Najib Ouakrim, Youssef Department of Mathematics and Computer Science ENS M2PA Laboratory University of Sidi Mohamed Ben Abdellah Fez30030 Morocco

ISBN: (纸本)9783031821523

The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of image processing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in image processing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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