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Difficulty-Aware Dynamic Network for Lightweight Exposure Correction

ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第6期34卷 5033-5048页

作者： Li, Ziwen Shao, Yuanjie Zhang, Feng Zhang, Jinpu Wang, Yuehuan Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China

Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.

关键词： image enhancement exposure correction difficulty awareness dynamic convolution

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Generative Artificial intelligence for Hyperspectral Sensor Data: A Review

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ieee JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution Machine learning Neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

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Guest Editorial: Special Issue on Emerging computational intelligence Techniques to Address Challenges in Biomedical Data and Imaging

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2023年第2期7卷 292-294页

作者： Tanveer, M. Lin, Chin-Teng Ting, Chuan-Kang Andreu-Perez, Javier Indian Inst Technol Indore Dept Math Simrol India Univ Technol Sydney Computat Intelligence & Brain Comp Interface Lab Ultimo NSW 2007 Australia Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 300 Taiwan Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

The papers in this special section focus on emerging computational intelligent techniques to address the challenges in biomedical data and image processing.

关键词： Special issues and sections Biomedical imaging computational intelligence Computed tomography computational modeling Bioinformatics Deep learning Data mining

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Eggs Separation Process by image processing. 18

Eggs Separation Process by Image Processing.

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18th International Joint symposium on Artificial intelligence and Natural Language processing (ISAI-NLP) / 4th International Conference on Artificial intelligence and Internet of Things (AIoT)

作者： Chumuang, Narumol Pinthong, Thidarat Muban Chombueng Rajabhat Univ Dep Digital Media Technol Ratchaburi Thailand Phetchaburi Rajabhat Univ Dep Business Informat Management Phetchaburi Thailand

ISBN: (纸本)9798350371215

The main objective of this research is the process of sorting chicken eggs using image processing techniques. The objective is to measure the size of chicken eggs. To control the separation of chicken eggs in the experiment, the researchers used the Edge Detection technique. The experiment took pictures of chicken eggs of different sizes, and the system was able to separate the eggs correctly.

关键词： Eggs Separation Process image processing Edge Detection

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Coarse-to-Fine Low-Light image Enhancement With Light Restoration and Color Refinement

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2024年第1期8卷 591-603页

作者： Wu, Xu Lai, Zhihui Yu, Shiqi Zhou, Jie Liang, Zhuoqian Shen, Linlin Shenzhen Univ Comp Vis Inst Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SZU Branch Shenzhen 518060 Peoples R China Coll Shandong Future Networks Res Inst Jinan 250002 Shandong Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

Low-light image enhancement aims to improve the illumination intensity while restoring color information. Despite recent advancements using deep learning methods, they still struggle with over or under-exposure in complex lighting scenes and poor color recovery in dark regions. To address these drawbacks, we propose a novel pipeline (called LRCR-Net) to perform Light Restoration and Color Refinement in a coarse-to-fine manner. In the coarse step, we focus on improving the illumination adaptively while avoiding inappropriate enhancement in the brighter or darker regions. This is achieved by introducing a region-calibrated residual block (RCRB) that balances local and global dependencies among different image regions. In the fine step, we aim to retouch the color of the images enhanced in the coarse step. To achieve this goal, we propose learnable image processing operators (LIPOs), including contrast and saturation operators, to refine the color according to the input images' color and contrast information. The final result is an image with proper illumination and rich color. Experiments on four benchmark datasets (NASA, LIME, MEF, and NPE) show that our model outperforms state-of-the-art methods.

关键词： Low-light image enhancement transformer learnable image processing operators

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Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind image Super-Resolution Supplementary Material

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ieee Transactions on image processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 ieee.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training Data collection Uncertainty Facial features Emotion recognition

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LYT-NET: Lightweight YUV Transformer-Based Network for Low-Light image Enhancement

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ieee SIGNAL processing LETTERS 2025年 32卷 2065-2069页

作者： Brateanu, Alexandru Balmez, Raul Avram, Adrian Orhei, Ciprian Ancuti, Cosmin Univ Manchester Manchester M13 9PL Lancs England Univ Politehn Timisoara Timisoara 300006 Romania West Univ Timisoara Timisoara 300223 Romania

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement. LYT-Net consists of several layers and detachable blocks, including our novel blocks-Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)-along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel $Y$ as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods.

关键词： Transformers Feature extraction image enhancement image restoration Training image color analysis Complexity theory Head computational modeling Convolution Low-light image enhancement vision transformer deep learning

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Viewport-Independent Blind Quality Assessment of AI-Generated Omnidirectional images via Vision-Language Correspondence

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ieee SIGNAL processing LETTERS 2025年 32卷 1630-1634页

作者： Liu, Xuelin Yan, Jiebin Lai, Chenyi Li, Yang Fang, Yuming Jiangxi Univ Finance & Econ Sch Comp & Artificial Intelligence Nanchang 330032 Peoples R China

The advancement of deep generation technology has significantly enhanced the growth of artificial intelligence-generated content (AIGC). Among these, AI-generated omnidirectional images (AGOIs), hold considerable promise for applications in virtual reality (VR). However, the quality of AGOIs varies widely, and there has been limited research focused on their quality assessment. In this letter, inspired by the characteristics of the human visual system, we propose a novel viewport-independent blindquality assessment method for AGOIs, termed VI-AGOIQA, which leverages vision-language correspondence. Specifically, to minimize the computational burden associated with viewport-based prediction methods for omnidirectional image quality assessment, a set of image patches are first extracted from AGOIs in Equirectangular Projection (ERP) format. Then, the correspondence between visual and textual inputs is effectively learned by utilizing the pre-trained image and text encoders of the Contrastive Language-image Pre-training (CLIP) model. Finally, a multimodal feature fusion module is applied to predict human visual preferences based on the learned knowledge of visual-language consistency. Extensive experiments conducted on publicly available database demonstrate the promising performance of the proposed method.

关键词： Visualization Feature extraction Databases Quality assessment Predictive models Training computational modeling Vectors Solid modeling image quality AI-generated omnidirectional image vision-language correspondence blind image quality assessment

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

2025 IEEE Symposium on Computational Intelligence in Health ...

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2025 ieee symposium on computational intelligence in Health and Medicine, CIHM 2025

ISBN: (纸本)9798331508333

The proceedings contain 17 papers. The topics discussed include: transforming medical practice: harnessing the power of big data and machine learning for predictive precision medicine;algebraic reproduction of triage decision making on simple heuristics of Boolean multivalued logic;deep learning on Hester Davis scores for inpatient fall prediction;comparative analysis and evaluation of well-being activity-infused fine-tuned language models with benchmark models;application of fuzzy-based uncertainty in cardiac MRI segmentation;images of stress: investigating image encodings and transfer learning for stress detection;training deep neural networks with multi-objective Adam optimizer for medical image classification;and deep-learning classifiers for small data orthopedic radiology.

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Automotive Scenarios for Trajectory Tracking using Machine Learning Techniques and image processing 17

Automotive Scenarios for Trajectory Tracking using Machine L...

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17th ieee International symposium on Applied computational intelligence and Informatics, SACI 2023

作者： Moga, Delia Filip, Ioan Politehnica University of Timisoara Department of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9798350321104

This paper presents a study on using innovative machine learning techniques that can be applied in automotive traffic scenarios to increase a vehicle's level of autonomy. The overtaking traffic scenario is treated for predicting the vehicle trajectory when overtaking another vehicle and the data is obtained by image processing using a video camera. Two different methods are compared, first by using classic tracking methods and a Kalman filter (as an adaptive filter) and second by using a machine learning technique - Support Vector Machine. The present article uses as inputs the data received from the camera and focuses on tracking selected objects and estimating their position using mainly image processing in automotive scenarios. The main purpose of this work is to experiment and compare different tracking modes to determine those that have the best performances in terms of runtime, memory usage and prediction accuracy. © 2023 ieee.

关键词： Support vector machines

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