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image Denoising Using a Deep Auto-encoder Approach Based on Beetle Antennae Search Algorithm 1

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2nd International conference on Computer and Communication Engineering, CCCE 2022

作者： Xiang, Qian Zhu, Peng College of Information Science and Engineering Wuchang Shouyi University Wuhan China

ISBN: (数字)9783031174223

ISBN: (纸本)9783031174216

image will be polluted by different kinds of noise in the process of acquisition, compression, and transmission, resulting in interference to subsequent image segmentation, feature extraction and other processing. With the development of deep convolutional neural network (DCNN), quite a few effective DCNNs have been designed and have made remarkable progress in image denoising. Gradient descent algorithm is generally used for DCNN training. However, due to the complex mathematical properties of the high-dimensional and non-convex loss optimization surface, there are often many local optimal points, saddle points or large range of gradient gentle regions, which affects training effect of the gradient descent algorithm. Although intelligent algorithms such as evolutionary algorithm have global optimization capability, they often have large computing resource requirements and slow convergence speed, which limit its application in DCNN training which is a high-dimensional optimization problem. Beetle antennae search (BAS) algorithm is a simple and efficient bionic intelligent optimization algorithm, which has global search ability. In this paper, the gradient descent method and BAS method are combined as a hybrid method for deep auto-encoder (DAE) denoising network training. Experimental results show that the proposed method accelerates the training speed of the DAE denoising network, reduces the blurring of edge details and improves the visual effect of the restored image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image enhancement

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Transforming Digital images into Cartoon Styles visual Using Convolutional Neural Networks 2

Transforming Digital Images into Cartoon Styles Visual Using...

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2nd International conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Balakannan, S.P. Celin, J.Jeya A Vijayan, M.Muthu Krishna, R.Harish Sivagurunathan, R. Mohan, N. Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu India

ISBN: (纸本)9798350355468

The art of cartoonization in images has garnered significant attention, especially with the breakthroughs witnessed in entertainment, digital art, and social *** traditional methods rely on the carted filters and edge detection techniques, which sometimes fail to retain the important features in the *** paper proposes a deep learning-based technique using convolutional neural network (CNN) to apply this cartoon *** description of CNN addition works-detection, feature extraction from real-world images, edge enhancement, color quantization, and finally achieve quality outputs as outputs.A diverse dataset of real as well as cartoon images was used for training our model, while it was optimized by loss functions such as MSE and perceptual losses. The proposed approach is evaluated for structural similarity, perceptual quality, and computational efficiency. Indexed databases demonstrated that our approach is better than traditional approaches as it creates appealing cartoon-type outputs while significantly keeping content integrity intact. This work will contribute to an ever-growing time with great relevance to various AI-enabled artistic transformations which can be of great use in digital media, animated storytelling, and creative content generation. © 2025 ieee.

关键词： AI Art Cartoonization Convolutional Neural Networks Deep Learning image processing

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Quantum of Corneal Scarring Calculation by image Analysis of Anterior Segment OCT images 4

Quantum of Corneal Scarring Calculation by Image Analysis of...

引用

4th ieee Eurasia conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Solomons, Cassandra D. Mathew, Ammu Anna Karthikeyan, V. Shanmugasundaram, Vivekanandan Thomas, Hannah Mary Sathya, A. Rose, Jeyanth School of Electrical Engineering Vellore Institute of Technology Vellore India Christian Medical College Dept. of Radiation Oncology Vellore India

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Anterior Segment OCT is a valuable tool in providing B scan images of the cornea and anterior segment of the eye. Corneal scarring is a residue of various ocular diseases which include corneal ulcers, trauma and dystrophies. Corneal scarring results in corneal blindness and severe visual disability for the affected patient. Current grades of assessing corneal scarring are purely qualitative in nature. Though these clinical grades (nebula, macula, leucoma) are useful in guiding clinicians in making clinical decisions regarding treatment, they lack precision because of the absence of numeric data. Experimental research involving corneal scarring would greatly benefit from quantitative methods which are amenable to statistical analysis. Purpose: To determine the quantum of corneal scarring by image analysis of Anterior Segment Optical Coherence Tomographic (AS-OCT) vertical and horizontal scans of both the scarred and normal corneas of 32 subjects. Methods: image processing and pixel analysis of the vertical and horizontal AS-OCT scans were done in the normal and scarred eyes to determine the mean pixel intensity, corneal thickness and quantum of corneal scarring. Results: The mean pixel intensity for scarred corneas was determined to be 126.2 for horizontal and 136.17 for vertical and 100.09 for horizontal, 96.75 for vertical on the normal corneas. The mean corneal thickness on scarred eyes was 204.46 and 217.38 for horizontal and vertical. Whereas on the normal corneas was 202.78 and 201.09 for horizontal and vertical. Quantum of variability for the scarred eyes was 26.11 and 39.42 for horizontal and vertical, respectively. The differences in medians of quantums of three scarred groups were statistically significant. © 2022 ieee.

关键词： image analysis

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Blind Point Cloud Quality Assessment via 3D visual Saliency and Point-Based Neural Network

Blind Point Cloud Quality Assessment via 3D Visual Saliency ...

引用

Workshops on image processing Theory, Tools and Applications, IPTA

作者： Salima Bourbia Ayoub Karine Aladine Chetouani Mohammed El Hassouni Maher Jridi LRIT-CNRST URAC 29 Mohammed V University in Rabat Faculty Of Sciences Rabat Morocco Université Paris Cité LIPADE Paris France PRISME Laboratory EA 4229 University of Orleans Orleans France FLSH FSR Mohammed V University in Rabat Rabat Morocco L@bISEN Vision-AD ISEN Yncréa Ouest Carquefou France

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

No-Reference/Blind Point Cloud Quality Assessment (NR-PCQA) aims to evaluate the quality of a point cloud in a manner similar to human judgment, without needing a reference. In the literature, most NR-PCQA methods do not accurately incorporate human visual information, which is crucial for quality evaluation. In this paper, we present a novel point-based NR-PCQA method that integrates 3D visual saliency into the quality assessment process. First, we generate a 3D saliency map, which is then used to weight the corresponding point cloud. After that, the salient point cloud is fed into a 3D point-based encoder to estimate the objective quality score. Extensive experiments conducted on two publicly available PCQA databases demon-strate that our proposed model surpasses existing state-of-the-art methods.

关键词： Point cloud compression Measurement visualization Three-dimensional displays Correlation Databases image processing Neural networks Benchmark testing Quality assessment

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A Novel Deep Learning Approach for Heliostat Dirt Segmentation and Severity Assessment

A Novel Deep Learning Approach for Heliostat Dirt Segmentati...

引用

ieee International conference on Signal processing, communications and Computing (ICSPCC)

作者： Wei Zhao Zhongtao Pan Tao Sheng Junfeng Zhang Yiming Xue Luoxiao Yang Department of New Energy Project Powerchina Northwest Engineering Corporation Limited Xi'an China Qinghai Branch Powerchina Renewable energy Corporation Limited Qinghai China Department of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Photothermal power generation is a promising technique for converting solar radiation into electricity with high efficiency and stability. However, the performance and maintenance of photothermal power plants depend on the cleanliness and reflectivity of the heliostats. This paper introduces an innovative approach to addressing the challenges of dirt detection and segmentation on heliostats. Leveraging the capabilities of deep learning, we propose the Multi-Scale Heliostat Dirt Segmentation and Classification (MSHDSC) framework, integrating a novel multi-scale feature fusion module (MSFFM) with an enhanced DeepLabV3+ network. This framework effectively segments small dirt areas on heliostat images, facilitating precise cleaning strategies. A unique aspect of our work is the introduction of an unsupervised clustering algorithm post-segmentation, which categorizes dirt based on color and texture, assigning a severity score to each category. This categorization assists in determining the cleaning complexity and prioritizing maintenance efforts. Experimental results show that our method outperforms several state-of-the-art image segmentation models in terms of accuracy and efficiency and provides useful information for targeted and prioritized cleaning of heliostats by robots or drones.

关键词： Deep learning image segmentation Accuracy Signal processing algorithms Clustering algorithms Cleaning Stability analysis Maintenance Power generation Surface treatment

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CKT: Cross-image Knowledge Transfer for Texture Anomaly Detection

CKT: Cross-Image Knowledge Transfer for Texture Anomaly Dete...

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ieee International conference on image processing

作者： Zixin Chen Xincheng Yao Zhenyu Liu Baozhu Zhang Chongyang Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Ningbo HTVision Digital Technology Co. Ltd Ningbo China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Most anomaly detection models are often sensitive to unavoidable disturbance or non-defective "visual defects", and such near abnormal samples are easily identified as anomalies, resulting in a high false detection rate. To this end, we propose a novel multi-scale Cross-image Knowledge Transfer anomaly detection model, namely CKT. Different from most existing intra-image distillation methods, our model transfers both the intra-image knowledge of the normal image and the inter-image knowledge of the normal image and the near-anomaly prototype, to assist the model to learn more robust normal patterns. Furthermore, we develop a cross-image attention module for explicitly enhancing the near-abnormal pattern learning during the distillation procedure, to alleviate the problem of high false detection rate induced by near-abnormal instances. Extensive experiments on texture datasets, such as KSDD2, MT, AITEX, and the textural subset of Mvtec-AD, show that the proposed CKT model can outperform most of the current unsupervised anomaly detection methods. Compared with the existing distillation based anomaly detection frameworks, our work can get significant gains with a margin of 2%.

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Research on EEG Feature Decoding Based on Stimulus image 4

Research on EEG Feature Decoding Based on Stimulus Image

引用

4th ieee Advanced Information Management, Communicates, Electronic and Automation Control conference, IMCEC 2021

作者： Qu, Liangcheng Chen, Dong Yin, Kuiying Nanjing Research Institute of Electronic Technology Nanjing China

ISBN: (纸本)9781728185347

Rapid serial visual presentation (RSVP) is a typical brain-computer interface system (BCI) paradigm in the target detection field. Electroencephalography (EEG) measured non-invasively BCI while associate with sparse target clips forms Event-related potentials (ERPs). Although ERP results from our brain's physiological neural response to the target, there is no direct evidence to prove that it is the brain's direct result to the visual target. The brain's complete processing information to the visual received image is still in the overall EEG information. This paper proposes a deep-learning method through a triple-RSVP paradigm to establish a correlation between EEG and stimulus images. Using LSTM to find the feature connection between the EEG and the stimulus image. The EEG feature associated with the image encoding feature is sent to the image decoder to obtain a decoded image similar to the stimulus image. The research in this article hopes to provide some new ideas for researchers in the field of brain science. © 2021 ieee.

关键词： Electroencephalography

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EventZoom: Learning to Denoise and Super Resolve Neuromorphic Events

EventZoom: Learning to Denoise and Super Resolve Neuromorphi...

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

作者： Duan, Peiqi Wang, Zihao W. Zhou, Xinyu Ma, Yi Shi, Boxin Peking Univ NELVT Dept Comp Sci & Technol Beijing Peoples R China Northwestern Univ Dept Comp Sci & Engn Evanston IL 60208 USA Peking Univ Inst Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665445092

We address the problem of jointly denoising and super resolving neuromorphic events, a novel visual signal that represents thresholded temporal gradients in a space-time window. The challenge for event signal processing is that they are asynchronously generated, and do not carry absolute intensity but only binary signs informing temporal variations. To study event signal formation and degradation, we implement a display-camera system which enables multi-resolution event recording. We further propose Event-Zoom, a deep neural framework with a backbone architecture of 3D U-Net. Event-Zoom is trained in a noise-to-noise fashion where the two ends of the network are unfiltered noisy events, enforcing noise-free event restoration. For resolution enhancement, EventZoom incorporates an event-to-image module supervised by high resolution images. Our results showed that EventZoom achieves at least 40x temporal efficiency compared to state-of-the-art (SOTA) event denoisers. Additionally, we demonstrate that EventZoom enables performance improvements on applications including event-based visual object tracking and image reconstruction. EventZoom achieves SOTA super resolution image reconstruction results while being 10x faster.

关键词： Training visualization image resolution Three-dimensional displays Neuromorphics Noise reduction image restoration

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High Speed Approximate Adder for Error Tolerant image processing Applications

High Speed Approximate Adder for Error Tolerant Image Proces...

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Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Siliveri Swetha Baddam Bharghavi Badisha Vasundara Nuka Siva Sankara Reddy Department of ECE CVR College of Engineering Ibrahimpatnam India Department of ECE Vasavi College of Engineering Mahidipatnam India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Approximate computing has emerged as a popular approach for improving the performance of the circuit and the system. It is particularly useful in applications where operations can tolerate errors, such as in scientific computing, signal processing, machine learning and multimedia processing. MGDI (Modified Gate Diffusion Input) is an enhanced version of GDI (Gate Diffusion Input) that offers full-logic functionality. In this study, a new 1-bit approximate MGDI-based full swing adder, known as Delay Efficient 1-bit MGDI (DEAFA) adder is proposed to minimize the error distance. This adder is extended to a 16-bit approximate adder, which is segmented into accurate and inaccurate adder circuits. The design is implemented on 90nm technology node using Cadence Virtuoso, with key design metrics of area, delay, and power consumption, used for performance evaluation.

关键词： Wireless communication Performance evaluation Power demand Scientific computing Multimedia computing Machine learning Logic gates Signal processing Delays Adders

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RFG-HDR: Representative Feature-Guided Transformer For Multi-Exposure High Dynamic Range Imaging

RFG-HDR: Representative Feature-Guided Transformer For Multi...

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ieee International conference on image processing

作者： Keuntek Lee Jaehyun Park Gu Yong Park Nam Ik Cho Department of Electrical and Computer Eng INMC Seoul National University Seoul Korea Interdisciplinary Program in Artificial Intelligence INMC Seoul National University Seoul Korea

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Multi-exposure fusion is a high dynamic range (HDR) imaging technique that combines multiple low dynamic range (LDR) images of a scene with varying exposure times to produce a single high-quality HDR image. Since each LDR frame is captured with a different exposure time (bias), it is crucial to extract meaningful features from each differently-exposed LDR frame for producing high-quality HDR images. This paper introduces a new contrastive learning method that provides a versatile way of extracting characteristic features from LDR frames by considering the relationship between LDR frames. Additionally, we introduce Representative Feature-Guided Transformer (RFGHDR), a new architecture that utilizes contrastive-learned representations to improve frame alignment and merging. Based on extensive experiments on various datasets, we have found that the RFG-HDR performs better than existing multi-exposure HDR imaging methods in terms of various evaluation metrics. Our work will be released on https://***/KeuntekLee/RFG-HDR.

关键词： Merging Imaging Learning (artificial intelligence) image representation Feature extraction Transformers communications technology

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