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A Two-Phase Reference-Free Approach for Low-Light image Enhancement

CIRCUITS systems AND SIGNAL processing 2024年第6期43卷 3553-3575页

作者： Chen, Jiale Lian, Qiusheng Shi, Baoshun Gao, Chengli Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao 066004 Hebei Peoples R China

Reference-free low-light image enhancement methods only employ low-light images during training, thereby significantly alleviating the over-reliance on obtaining paired or unpaired datasets. Existing reference-free low-light image enhancement approaches still struggle to strike a balance between enhancing vivid color and suppressing noise in low-light images. To mitigate such issues, we propose a novel deep learning-based reference-free method that contains two phases, separating the low-light image enhancement into decomposition and refinement problems. In the decomposition phase, we present a value channel prior based on histogram equalization on HSv color space, termed as v-HE prior. Inspired by retinex theory, v-HE prior guides the decomposition network (Dec-Net) to estimate the reflectance component of the value channel. To further refine the pre-enhanced result, we construct a structure-aware loss to guide the refinement network (Ref-Net) in the refinement phase. We conduct extensive experiments to verify the effectiveness of the proposed method, qualitatively and quantitatively. Compared with other reference-free algorithms, our approach effectively addresses the challenges of low-light image enhancement and significantly improves image quality.

关键词： Low-light image enhancement Deep neural network Reference-free Retinex theory HSv color space

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Simulating the influence of optical-system parameters on the error in determining the orientation and position of a fiducial marker

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JOURNAL OF OPTICAL TECHNOLOGY 2024年第7期91卷 479-484页

作者： Shmatko, Ekaterina, v Sivov, N. I. K. I. T. A. Yu. Eremin, Danil, v Poroykov, A. N. T. O. N. Yu. Natl Res Univ Moscow Power Engn Inst Moscow Russia

Subject of study. This study investigates the influence of optical-system parameters on the error in determining the orientation and position of fiducial markers. Aim of study. This study determines the dependencies of absolute error in position and orientation on various influencing factors. Method. The error in a machine-vision system is assessed based on fiducial markers using computer-image modeling in the Unity 3D graphics system. Main results. Over 100,000 images of AprilTag markers in different positions and orientations were synthesized and processed during the simulation. The results of this simulation yielded the dependencies of absolute position and orientation errors on the distance between the camera and marker, the rotation angle of the marker, and the focal lengths of the camera. Practical significance. The obtained results may be utilized to optimize the placement of markers on the platform, select the optimal video camera positions and lens focal lengths, and implement adjustments in the image-processing algorithm. These changes can improve measurement accuracy in systems used for developing orientation algorithms for microsatellites. (c) 2024 Optica Publishing Group

关键词： Camera calibration Computer simulation High speed photography Machine vision Optical systems video

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CECF: A DNN-Based Energy-Efficient Cloud-Edge Collaboration Framework for Intelligent Workload Scheduling in 6G-Enabled Transportation systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Lu, Yao Liu, Lu Panneerselvam, John Gu, Jiayan Garraghan, Peter Min, Geyong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF England Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230000 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

The rapid growth of Internet of vehicle (Iov) devices and Artificial Intelligence (AI) applications has accelerated the adoption of Cloud and Edge Computing. The advent of sixth-generation mobile communication technology (6G) further facilitates the deployment of Cloud-Edge collaborative computing in large-scale Intelligent Transportation systems (ITS). Effective ITS must efficiently handle both latency-sensitive tasks (e.g., obstacle detection, traffic signal recognition) and computationally intensive tasks (e.g., path optimization, traffic flow prediction). However, existing Cloud-Edge collaborative frameworks struggle to accurately classify diverse workloads and provide efficient low-latency processing, leading to energy inefficiencies and task failures. To address these challenges, this paper introduces a Deep Learning-based Cloud-Edge Collaboration Framework (CECF) designed to optimize energy conservation in Cloud and Edge environments. CECF employs a DNN-based classifier to categorize workloads for processing in the Cloud or Edge. The classified tasks are managed by a dedicated Cloud scheduler (DSGA) and an Edge scheduler (EA-DFPSO), respectively. To enhance scheduling efficiency for highly variable Cloud tasks, DSGA incorporates a novel self-adaptive mutation algorithm and a random point fixed distance crossover method. Extensive evaluations using real-world workload traces demonstrate that CECF achieves up to a 8.5% improvement in system reliability and reduces energy consumption by 35.88% compared to baseline approaches.

关键词： Cloud computing Servers Energy consumption Collaboration Energy efficiency image edge detection Heuristic algorithms Scheduling algorithms Real-time systems virtual machines Cloud-edge collaboration computing Internet of vehicles (Iov) deep learning intelligent transportation systems

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Advances of the Scientific School of vL Arlazarov in Dataset Creation and Training Sample Synthesis for Solving Modern Computer vision Problems

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PATTERN RECOGNITION AND image ANALYSIS 2023年第4期33卷 730-742页

作者： Chernyshova, Y. S. Sheshkus, A. v. Bulatov, K. B. Arlazarov, v. v. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119133 Russia Smart Engines Serv LLC Moscow 121205 Russia

This paper considers a scientific school of synthesis of samples and creation of datasets, which is a part of the family of scientific schools associated with image processing and analysis, originating from the work of a team led by Prof. v.L. Arlazarov in the 1970s. As part of the work of the school, the researchers have obtained important fundamental and applied results as well as set new research tasks. Over the years of the school's existence the scientific team has developed several algorithms and systems for the synthesis and augmentation of image samples. Moreover, they have created and published more than ten open annotated image datasets, including the unique MIDv dataset family that contains synthesized images of identity documents and is the first in the world to allow a full open comparison of recognition systems for such documents.

关键词： scientific school image synthesis sample augmentation open data sets

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Adversarial Attack Detection via Fuzzy Predictions

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IEEE TRANSACTIONS ON FUZZY systems 2024年第12期32卷 7015-7024页

作者： Li, Yi Angelov, Plamen Suri, Neeraj Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England

image processing using neural networks act as a tool to speed up predictions for users, specifically on large-scale image samples. To guarantee the clean data for training accuracy, various deep learning-based adversarial attack detection techniques have been proposed. These crisp set-based detection methods directly determine whether an image is clean or attacked, while, calculating the loss is nondifferentiable and hinders training through normal back-propagation. Motivated by the recent success in fuzzy systems, in this work, we present an attack detection method to further improve detection performance, which is suitable for any pretrained neural network classifier. Subsequently, the fuzzification network is used to obtain feature maps to produce fuzzy sets of difference degree between clean and attacked images. The fuzzy rules control the intelligence that determines the detection boundaries. Different from previous fuzzy systems, we propose a fuzzy mean-intelligence mechanism with new support and confidence functions to improve fuzzy rule's quality. In the defuzzification layer, the fuzzy prediction from the intelligence is mapped back into the crisp model predictions for images. The loss between the prediction and label controls the rules to train the fuzzy detector. We show that the fuzzy rule-based network learns rich feature information than binary outputs and offer to obtain an overall performance gain. Experiment results show that compared to various benchmark fuzzy systems and adversarial attack detection methods, our fuzzy detector achieves better detection performance over a wide range of images.

关键词： Detectors Neural networks Feature extraction Training Fuzzy systems Fuzzy sets Accuracy Robustness Predictive models Prediction algorithms Adversarial attack detection confidence function fuzzification fuzzy mean-intelligence (FZ-I) neural network

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Application of Cascade Methods as a Universal Object Detection Tool

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PATTERN RECOGNITION AND image ANALYSIS 2023年第4期33卷 685-698页

作者： Matalov, D. P. Usilin, S. A. Nikolaev, D. P. Arlazarov, v. v. Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow 119333 Russia Smart Engines Serv LLC Moscow 121205 Russia Russian Acad Sci Inst Informat Transmiss Problems Moscow 127051 Russia

This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor vladimir L'vovich Arlazarov, in the field of development and application of the viola-Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the viola-Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems.

关键词： machine learning viola-Jones method scientific school image processing edge computing object detection image classification image analysis statistical recognition methods

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Emergence Model of Perception With Global-Contour Precedence Based on Gestalt Theory and Primary visual Cortex

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IEEE TRANSACTIONS ON image processing 2025年 34卷 2721-2736页

作者： Li, Jingmeng Wei, Hui Fudan Univ Sch Comp Sci Lab Algorithms Cognit Models Shanghai 200438 Peoples R China Fudan Univ Innovat Ctr Callig & Painting Creat Technol Sch Comp Sci Lab Algorithms Cognit ModelsMCT Shanghai 200438 Peoples R China

Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions caused by occlusion and noise in natural images. In this paper, we present GPGrouper, a perceptual edge grouping model based on gestalt theory and the primary visual cortex (v1). Different from the existing methods, GPGrouper leverages the edge representation and grouping matrix (ERGM), a functional structure inspired by v1 mechanisms, to represent edges in a way that can effectively reduce grouping errors caused by occlusion between objects. ERGM is trained with natural image contours and further provides a priori guidance for the construction of the edge connection graph (ECG) that is useful to minimize the impact of noise on grouping. In the experiment, we compared GPGrouper and the state-of-the-art (SOTA) method of perceptual grouping on the visual psychology pathfinder challenge. The results demonstrate that GPGrouper outperforms the SOTA method in grouping performance. Furthermore, in the grouping experiments involving line segments with varying lengths detected by the Line Segment Detector (LSD), as well as those involving superpixel segmentation results with significant levels of interfering noise using the SLIC algorithm, GPGrouper was superior to the existing methods in terms of grouping effect and robustness. Moreover, the results of applying the grouping results to the vision tasks objectness demonstrate that GPGrouper can contribute significantly to high-level visual tasks.

关键词： image edge detection visualization Computational modeling Noise image segmentation visual systems Psychology Organizations vectors Object detection Perceptual organization global precedence effect Gestalt theory primary visual cortex perceptual edge grouping

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Effective in-the-Moment image Compression on FPGA: A Combinatorial Method Including DWT and IWT Techniques 10

Effective in-the-Moment Image Compression on FPGA: A Combina...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Dinesh, U. Rajan, v. Deepak Navaneethan, S. Saveetha Engineering College Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350384369

This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situations where resources are limited, the flexibility and adaptability offered by the FPGA architecture are ideal. Our technique strikes a compromise between compression effectiveness and image quality by utilizing DWT for multi-resolution analysis and IWT for spatial redundancy reduction. Real-time processing and resource optimization are ensured by the FPGA implementation. FPGA-optimized algorithms that tackle resource constraints are among the contributions. Evaluations demonstrate enhanced signal-to-noise ratios, compression ratios, and execution times. This study highlights how fast FPGA can compress images, especially for embedded systems and space missions. The study not only improves image compression but also highlights how FPGA can be used to increase the effectiveness of signal processing algorithms. © 2024 IEEE.

关键词： Signal to noise ratio

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Optical technologies and the visual picture of the world: iconics and neuroiconics

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JOURNAL OF OPTICAL TECHNOLOGY 2022年第8期89卷 434-436页

作者： Shelepin, Yu E. Lutsiv, v. R. Korotaev, v. v. Russian Acad Sci Pavlov Inst Physiol St Petersburg Russia St Petersburg State Univ Aerosp Instrumentat St Petersburg Russia ITMO Univ St Petersburg Russia

A definition of neuroiconics is proposed as a branch of science at the intersection of human and animal physiology and iconics that studies neurophysiological processes and algorithms for processing video information ... 详细信息

关键词： algorithms image processing Neural networks Pattern recognition Thermal imaging visual system

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Landslide prediction with severity analysis using efficient computer vision and soft computing algorithms

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Multimedia Tools and Applications 2024年第37期83卷 85079-85101页

作者： varangaonkar, Payal Rode, S.v. Sipna College of Engineering and Technology Amravati India Electronics and Telecommunication Department Sipna College of Engineering & ampTechnology Amravati India

Since the preceding decade, there has been a great deal of interest in forecasting landslides using remote-sensing images. Early detection of possible landslide zones will help to save lives and money. However, this approach presents several obstacles. Computer vision systems must be carefully built since normal image processing does not apply to images obtained by remote sensing (RS). This research proposes a novel landslide prediction method with a severity analysis model based on real-time hyperspectral RS images. The proposed model consists of phases of pre-processing, dynamic segmentation, hybrid feature extraction, landslide prediction, and landslide severity detection. The pre-processing step performs the geometric correction of input RS images to suppress the built-up regions, water, and vegetation using the Normal Difference vegetation Index (NDvI). The pre-processing stage encompasses many steps, including atmospheric adjustments, geometric corrections, and the elimination of superfluous regions by denoising techniques such as 2D median filtering. Dynamic segmentation is employed to segment the pre-processed picture for Region of Interest (ROI) localization. The ROI image is utilized to extract manually designed features that accurately depict spatial and temporal variations within the input RS image. For each input RS image, the hybrid feature vector is normalized. We trained ANN and SvM to predict landslides. If the input image predicts a landslide, its severity is identified. For the performance analysis, we collected real-time RS images of the western region of India (Goa and Maharashtra). Simulation results show the efficiency of the proposed model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer vision

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