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Enhancing CLAHE with Interval-Valued Fermatean Fuzziness for Robust Low-Light image Enhancement

Enhancing CLAHE with Interval-Valued Fermatean Fuzziness for...

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ieee International conference on Data Science in Cyberspace (DSC)

作者： Hongpeng Wang Duanfa Wang Zhiqin Wang Chenglong Li School of Computer Science and Technology Shandong Jianzhu University Jinan China Shandong Pharmaceutical Glass Co. Ltd Zibo China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

image processing, as the foundation of computer vision, plays a crucial role in various important fields such as aerospace, medical imaging, and military operations. However, the images captured under extreme lighting conditions often suffer from issues like uneven brightness and contrast, loss of details, and color degradation, posing challenges for subsequent visual tasks such as semantic analysis, image segmentation, and object recognition. The mainstream Contrast-Limited Adaptive Histogram Equalization (CLAHE) method fails to achieve effective results when pixel intensities approach 0, and parameter selection remains a significant challenge. To address the problem of image quality degradation under extreme lighting conditions and the limitations of CLAHE, this paper proposes an Interval-Valued Fermatean Fuzzy CLAHE (IVFF-CLAHE) method for low-light image enhancement. The proposed method designs several Fermatean fuzzy generators and interval Fermatean generators to perform fuzzy processing and optimize the gray level distribution of the image. Subsequently, it applies fuzzy CLAHE to the values in the HSV color space for histogram equalization. For parameter selection, a modified Dynamic Weight Particle Swarm Optimization (DW-PSO) algorithm is utilized to improve the efficiency of optimal parameter search. Through comparative experiments with existing low-light image enhancement algorithms based on fuzzy principles, the proposed method demonstrates significant improvements in subjective contrast and brightness, with better preservation of details and texture, resulting in more natural and clean images. Objectively, it achieves the highest performance compared to the evaluated techniques in terms of information richness, contrast, brightness, colorfulness, and image quality metrics.

关键词： Measurement image quality Histograms image color analysis Heuristic algorithms Brightness Lighting Generators image enhancement Optimization

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A Comparative Study of Text-to-image Generative Models

A Comparative Study of Text-to-Image Generative Models

引用

International Symposium on Signal, image, Video and communications (ISIVC)

作者： Imran Shafiq Ahmad Nazia Siddiqui Boubakeur Boufama School of Computer Science University of Windsor Windsor ON Canada

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

Recent advancements of deep learning (DL) techniques have revolutionized various fields such as computer vision, image processing, artificial intelligence, and natural language processing. One notable application that showcases the power of these algorithms is the field of image synthesis, where new images are created from textual descriptions. Generative models play a crucial role in this process, enabling the generation of novel data based on patterns learned from the training set. Diffusion models are a distinctive class of generative models that operate by introducing random noise to existing data, and subsequently learning to reverse this diffusion process. This technique is particularly valuable in scenarios where the transformation of data over time or through sequential steps is a critical aspect of the generation process. The ability to translate textual descriptions into visual representations offers new possibilities for human-computer interaction and creative expressions. This paper provides a comparison and analysis of generative adversarial networks (GANs) and diffusion models within the domain of “text-to-image generation” to understand the strength and weaknesses of different models in specific contexts. For this purpose, we are using a combination of Vector-Quantized GAN (VQGAN) and Contrastive Language-image Pre-training (CLIP) model. This combination provides a powerful integration of two distinct machine learning (ML) techniques for the purpose of creating images from textual input. Guided Language to image Diffusion for Generation and Editing (GLIDE) is the diffusion model used in this study. For both models, text input from the MS-COCO data set is used. Evaluation of generated images is performed using Fréchet Inception Distance (FID) and Inception Score (IS) metrics. Semantic object accuracy score (SOA) is also used as a metric to add an additional layer for analysis by considering the relevance of the generated images to the provided captions

关键词： Measurement Training Human computer interaction visualization image synthesis Semantics Noise

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Convolutional Neural Network with Attention Mechanism for image-Based Smoke Detection 4

Convolutional Neural Network with Attention Mechanism for Im...

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4th International conference on Advances in Computer Technology, Information Science and communications, CTISC 2022

作者： Chen, Hanyang Ji, Qingge Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Guangzhou510006 China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Smoke detection based on computer vision plays a key role in fire prevention. With the development of deep learning, convolutional neural network frameworks are increasingly used in this field. However, due to the large span of smoke's scale and the variety of its color as well as its texture, the features are even more complex. In addition, the sampling of features by the existing neural networks is limited to the size of the convolution kernel. It is difficult to extract the global features. So, detecting smoke from pictures is still a problem. In this work, we proposed a feature-fusion method based on local and global attention mechanisms. It is able to better fuse the low-level and high-level features. In order to solve the scale problem, we proposed a mechanism for selection and offset of the receptive field. In addition, we combine the transformer based on self-attention mechanism and convolution operation to make the model aware of global information. Finally, we optimize the sampling of feature maps by the detection head of the one-stage network based on the smoke color features. Experiments show that the proposed model outperforms state-of-the-art relevant competitors by 4.6% on AP.5:.95. © 2022 ieee.

关键词： Smoke

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LL-ICM: image Compression for Low-Level Machine Vision via Large Vision-Language Model

LL-ICM: Image Compression for Low-Level Machine Vision via L...

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2025 Data Compression conference, DCC 2025

作者： Xue, Yuan Zhang, Qi Jia, Chuanmin Wang, Shiqi School of Computer Science City University of Hong Kong Hong Kong Peking University National Engineering Research Center of Visual Technology China Wangxuan Institute of Computer Technology Peking University China

ISBN: (纸本)9798331534714

image Compression for Machines (ICM) aims to compress images for machine vision tasks, while current methods mostly focus on the demands for high-level tasks. However, the quality of original images is usually not guaranteed in the real world, leading to even worse downstream task performance after compression. Thus, lowlevel (LL) restoration tasks should also be considered in ICM. In this paper, we propose the first ICM framework for LL machine vision tasks, namely LL-ICM, which optimizes the compression and LL processing performance simultaneously. Moreover, LL-ICM leverages large vision-language model (VLM) to solve different LL task within a single model, which is particularly useful when the distortion type of the original image is uncertain. As illustrated in Fig. 1(a), LL-ICM consists of a neural image codec and a VLM-based LL processing module. Given an original image with distortions, LL-ICM firstly compress it as X. Then, we extract a generalized feature F from X , which is then encoded as two representations, distortion type f and caption s. After that, the LL processing module receives X and its representations to generate the restored version of X, i.e., XH. © 2025 ieee.

关键词： image compression

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AdderSR: Towards Energy Efficient image Super-Resolution

AdderSR: Towards Energy Efficient Image Super-Resolution

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Song, Dehua Wang, Yunhe Chen, Hanting Xu, Chang Xu, Chunjing Tao, Dacheng Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5x reduction on the energy consumption. The codes are available at: https://***/huawei-noah/AdderNet.

关键词： Energy consumption visualization Computational modeling Superresolution Refining Neural networks Pattern recognition

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OCR Text Recognition and Recommendation Using Machine Learning

OCR Text Recognition and Recommendation Using Machine Learni...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Janani, M. Laya, H. Mathishree, B. St. Joseph's College of Engineering Dept of It Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

The proposed system creates Ayurvedic and pharmaceutical alternatives using sophisticated text extraction and analysis methods. Text is extracted from PDFs or images using OCR, and fuzzy matching is applied to identify ingredient names correctly by mapping them to their actual forms. More advanced techniques like KNN and decision trees are used to propose different methods for a drug's composition and action. NLP boosts data extraction and interpretation by connecting diseases with relevant drugs, making recommendations more accurate. The system is based on a light-weight Flask backend for processing user requests, querying a structured database, and producing context-sensitive recommendations. Besides providing Ayurvedic medicine-based options, the platform broadens patient options, enhancing healthcare solution accessibility and affordability. Several pre-processing methods, such as noise reduction and image alignment, are used to maximize OCR accuracy. When faced with low OCR outputs or user errors, fuzzy matching again enhances text recognition. With its ability to offer dependable and informed healthcare options, this site has the capacity to transform the accessibility of healthcare by providing pharmaceutical as well as traditional medicine alternatives. © 2025 ieee.

关键词： Natural language processing systems

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Multi-View image Enhancement Inconsistency Decoupling Guided 3D Gaussian Splatting

Multi-View Image Enhancement Inconsistency Decoupling Guided...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Lu Xiao Jiahao Wu Zhanke Wang Guanhua Wu Runling Liu Zhiyan Wang Ronggang Wang Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology Shenzhen Graduate School Peking University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D Gaussian Splatting (3DGS) has recently made breakthrough progress in radiance field reconstruction but struggles with multi-view inconsistency. Modern cameras often apply tailored enhancements to each view when capturing multi-view images. While this improves individual image quality, it inevitably disrupts the multi-view consistency assumption, particularly in challenging conditions like nighttime scenes. This disrupts 3DGS’s ability to accurately reconstruct the scene, resulting in the emergence of artifacts such as "floaters". To tackle this challenge, we propose equipping each training view with a 4D bilateral affine grid to decouple the inconsistencies across multi-view images, guiding more accurate 3DGS reconstruction. To solve the problem of less visually appealing renderings caused by the absence of certain enhancements, our 4D bilateral affine grid also serves a dual purpose: it propagates enhanced features from a single edited image throughout the entire 3D scene, allowing for comprehensive 3D editing with just one image. Experiments demonstrate that our method not only effectively decouples inconsistencies in multi-view images, enabling more precise 3DGS reconstruction, but also facilitates refined 3D editing, further improving the visual quality of otherwise monotonous scenes.

关键词： Training Photography image quality visualization Three-dimensional displays Accuracy Speech enhancement Signal processing Rendering (computer graphics) image reconstruction

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Autonomous Boundary Detection Using image Recognition for Robotic Lawn Mower 25

Autonomous Boundary Detection Using Image Recognition for Ro...

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25th International conference on Mechatronics Technology, ICMT 2022

作者： Wu, Meng-Huai Chang, Chia-Wei Yu, Jyh-Cheng National Kaohsiung University of Science and Technology Department of Mechatronics Engineering Kaohsiung Taiwan

ISBN: (数字)9781665461955

ISBN: (纸本)9781665461955

Most robotic lawnmowers adopt buried metal wires to define their working area, which is labor and time-consuming. This study applies the fusion of multiple sensors, including an inertial measurement unit (IMU), wheel encoders, light detection and ranging (LiDAR), and an RGB-D camera, to automatically detect the boundary of the lawn and construct the navigation map. visual simultaneous localization and mapping (vSLAM) are adopted based on an RGB-D camera. For the boundary pavement or obstacles lower or similar to grass height, a support vector machine (SVM) is applied to identify boundaries and obstacles and establish the augmented Octomap with virtual walls. A set of image processing methods is proposed to lower the computing load of the embedded system and transfer the image data into point cloud data to construct the navigation map. The robot operating system (ROS)-based control system of the prototype was built using the embedded system (Jetson Nano, NVIDIA;Santa Clara, CA, USA) and control boards (ARM STM32, STMicroelectronics;Geneva, Switzerland). The proposed method could be further used in the process of map construction and navigation. © 2022 ieee.

关键词： Support vector machines

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I See What You Hear: A Vision-Inspired Method to Localize Words

I See What You Hear: A Vision-Inspired Method to Localize Wo...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mohammad Samragh Arnav Kundu Ting-Yao Hu Aman Chadha Ashish Srivastava Minsik Cho Oncel Tuzel Devang Naik Apple

This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contemporary literature for visual data. Noting that an audio can be interpreted as a 1-dimensional image, object localization techniques can be fundamentally useful for word localization. Building upon this idea, we propose a lightweight solution for word detection and localization. We use bounding box regression for word localization, which enables our model to detect the occurrence, offset, and duration of keywords in a given audio stream. We experiment with LibriSpeech and train a model to localize 1000 words. Compared to existing work [1], our method reduces model size by 94%, and improves the F1 score by 6.5%.

关键词： Location awareness visualization Buildings Object detection Streaming media Signal processing Acoustics

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Machine Learning-Based Low-Complexity image Feature Descriptor for MPEG-CDVS Standard

Machine Learning-Based Low-Complexity Image Feature Descript...

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ieee International conference on Knowledge Innovation and Invention (ICKII)

作者： You-Wei Jiang You-Rong Chen Shih-Hsiang Lin Pei-Yin Chen Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

Compact descriptors for visual search (CDVS) is a completed standard from the ISO/IEC moving pictures experts group (MPEG). CDVS has a low complexity and bitrate efficiency on image matching and retrieval. The MPEG CDVS framework detects key feature points in the image and extracts descriptors to obtain bitstreams to match images. We proposed a low-complexity image descriptor for visual searching within the MPEG CDVS framework. The proposed algorithm integrated traditional image processing methods with a machince learning (ML) network, resulting in reduced compression time and the size of the CDVS standard bitstream. To evaluate the effectiveness of the proposed algorithm, we tested the proposed algorithm with the CDVS standard software to ensure its compatibility. On the CDVS-Benchmark, the experimental results demonstrated that the proposed algorithm reduced matching time by 31.7%, bitstream size by 31.8%, and extracting time by 12.8% compared to the CDVS standard test model. Similarly, on the HPatches-Benchmark, the proposed algorithm reduced matching time by 5.3%, bitstream size by 25.2%, and extracting time by 32.0% compared to the CDVS standard test model.

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