Article
Computer Science, Information Systems
Nana Yu, Jinjiang Li, Zhen Hua
Summary: This paper proposes a decolorization method based on contrast pyramid transform fusion, which can effectively convert a color image into a grayscale image while maintaining the contrast and structural characteristics of the original image. By performing image fusion and reconstruction on the original color image and the initial grayscale image, the proposed method can fully preserve image details and reduce distortion in the fusion and decolorization process.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Enqi Zhang, Lihong Guo, Junda Guo, Shufeng Yan, Xiangyang Li, Lingsheng Kong
Summary: This paper proposes a low-brightness image enhancement algorithm based on multi-scale fusion. By using brightness transformation and illumination estimation techniques, advantageous features are extracted and images are fused to improve image quality. Experimental results demonstrate that the proposed method has better enhancement effect.
APPLIED SCIENCES-BASEL
(2023)
Article
Environmental Sciences
Weihong Zhang, Xiaobo Li, Shuping Xu, Xujin Li, Yiguang Yang, Degang Xu, Tiegen Liu, Haofeng Hu
Summary: This paper introduces an underwater color image processing approach that effectively enhances contrast and colors in underwater images by combining frequency and spatial domains. The method addresses challenges such as low contrast, color distortion, and obscured details in underwater images. Experimental results show that the proposed method outperforms other methods in terms of enhancing contrast and rendering natural colors.
Article
Multidisciplinary Sciences
Jingmin An, Yong Du, Peng Hong, Lei Zhang, Xiaogang Weng
Summary: Insect pest recognition is an important field in agriculture and ecology, but the slight variations in appearance among different insect species make it difficult for human experts to recognize them. Therefore, the use of machine learning methods for insect recognition is becoming increasingly important. In this study, we proposed a feature fusion network that combines feature representations from different backbone models. We used CNN-based ResNet, attention-based Vision Transformer, and Swin Transformer backbones to localize important insect image regions. We also developed an attention-selection mechanism that integrates these important regions to reconstruct attention areas and improve insect recognition.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Meenakshi Pawar, Sanjay Talbar
Summary: Early detection of breast cancer is crucial for survival, and contrast enhancement techniques can provide accurate segmentation of mammogram images. The DWT coefficient fusion based on local entropy maximization algorithm shows superior performance compared to other contrast enhancement methods, improving edge contents, contrast measure, similarity index, and brightness error values.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Haoxiang Lu, Zhenbing Liu, Rushi Lan, Xipeng Pan, Junming Gong
Summary: This paper proposes a new and efficient approach named MFMR for enhancing lowlight images by using the hue-saturation-value (HSV) colour space. The approach includes the estimation of artifact-free illumination and reflection component, adaptive bi-interval histogram and morphological operations for processing, adaptive gamma correction, and adaptive multi-scale fusion strategy for generating high-quality images. The experiments show that this method outperforms state-of-the-art comparison methods and can generate satisfying images in severe conditions such as heavy fog and underwater.
IET IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Hao-Tian Wu, Kaihan Zheng, Qi Huang, Jiankun Hu
Summary: A new hierarchical contrast enhancement scheme for MR brain images with reversibility is proposed, utilizing deep learning for automatic tissue segmentation and guidance in the enhancement process, resulting in better enhancement effects and visual quality compared to conventional methods. Evaluation results demonstrate the effectiveness of the proposed scheme in enhancing interested tissues in MR brain images.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Review
Computer Science, Artificial Intelligence
Smitha Raveendran, Mukesh D. Patil, Gajanan K. Birajdar
Summary: Research in underwater image processing has increased significantly in the last decade due to human dependence on valuable underwater resources. Excellent methods for underwater image enhancement are essential for effective exploration of the underwater environment. This article presents an overview of various underwater image enhancement techniques and their classifications, as well as the importance of underwater datasets and evaluation metrics for quantitative assessment.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Information Systems
Simi Venuji Renuka, Damodar Reddy Edla, Justin Joseph
Summary: This paper proposes an objective statistic called CQCEI to assess the quality of contrast enhancement on MR images. CQCEI takes into account various aspects of image quality related to contrast enhancement, including improvement in contrast, shift in mean brightness, saturation, and noise-amplification. The results show that CQCEI is in good agreement with subjective fidelity ratings on contrast-enhanced MR images.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Review
Computer Science, Artificial Intelligence
Gaurav Choudhary, Dinesh Sethi
Summary: Image fusion is a well-established field of study in digital image processing. Multi-focus image fusion (MFIF) is a widely used application, but it faces challenges such as low contrast, color distortion, and fusion losses. This study proposes an experimental and comprehensive approach to address these challenges by applying pre-hand enhancement criteria for both grayscale and color images. The results demonstrate that using the enhancement method as a preprocessing step can improve the outcomes of MFIF algorithms in both objective and subjective evaluations.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Xiaoning Liu, Hui Li, Ce Zhu
Summary: In this work, we propose an efficient framework for image dehazing, which involves contrast enhancement and exposure fusion stages. The proposed method outperforms state-of-the-art methods in terms of visual and quantitative evaluation.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Automation & Control Systems
Shunmin An, Lihong Xu, Zhichao Deng, Huapeng Zhang
Summary: The article introduces a hybrid fusion method for underwater image enhancement, which solves the problems of white balance distortion, color shift, low visibility, and low contrast. Through experiments, the proposed method achieves optimal results in the application of geometric rotation estimation, feature point matching, and edge detection.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Computer Science, Information Systems
Lalit Maurya, Viney Lohchab, Prasant Kumar Mahapatra, Janos Abonyi
Summary: Many vision-based systems suffer from poor levels of contrast and brightness due to inadequate and improper illumination during image acquisition. By using nature-inspired optimization, a balance between contrast and brightness can be achieved in image enhancement, improving image quality.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Engineering, Electrical & Electronic
Yong Yang, Danjie Zhang, Weiguo Wan, Shuying Huang
Summary: A novel multiscale exposure image fusion method based on multivisual feature measurement and detail enhancement is proposed, which achieves better fusion performance by measuring the visual features of the source images and optimizing the weight maps.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Dong Han, Liang Li, Xiaojie Guo, Jiayi Ma
Summary: This paper presents a deep perceptual enhancement network for multi-exposure image fusion, which focuses on informativeness and visual realism, with two modules for content details and color mapping/correction, demonstrating its superiority over other state-of-the-art alternatives both quantitatively and qualitatively. The proposed strategy shows flexibility in improving exposure quality of single images, and can fuse 720p images in more than 60 pairs per second on an Nvidia 2080Ti GPU, making it practical for use.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.