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Unsupervised low-light image Enhancement with Quality-Task-Perception Loss

Unsupervised low-light Image Enhancement with Quality-Task-P...

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International Joint conference on Neural Networks (IJCNN)

作者： Guo, Haoming Xu, Wei Qiu, Song East China Normal Univ Sch Commun & Elect Engn 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ Engn Res Ctr Software Hardware Codesign Technol & Minist Educ 500 Dongchuan Rd Shanghai 200241 Peoples R China

ISBN: (纸本)9780738133669

Unsupervised low-light image enhancement is a classic task in image processing area. Deep learning based methods are the main approaches to tackle this task due to their effectiveness in whole visual areas. However, blur image or fake color, insufficient enhancement and missing semantic information are three common issues when utilize these methods. In this paper, we propose a new hybrid loss named Quality-Task-Perception (QTP) to alleviate these three problems simultaneously. Specifically, this hybrid loss includes three components: quality, task and perception. The quality part overcomes the blur image or fake color problem by enforcing the similarity between image quality scores of enhancement results and those of samples in daytime domain. The task part tackles the insufficient enhancement issue by constraining enhancement results to have higher daytime probabilities, where daytime probabilities are achieved by our proposed new pipeline. The perception part copes with the missing semantic information by restricting the phase consistency between the low-light image and its enhanced version. As this new hybrid loss is special designed to deal with these three problems, a more accurate low-light image enhancement model can be learned. Comprehensive experiments on both supervised and unsupervised datasets demonstrate the effectiveness of our proposed approach.

关键词： low-light image enhancement deep learning QTP

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Unifying Vision, Text, and Layout for Universal Document processing

Unifying Vision, Text, and Layout for Universal Document Pro...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zineng Tang Ziyi Yang Guoxin Wang Yuwei Fang Yang Liu Chenguang Zhu Michael Zeng Cha Zhang Mohit Bansal University of North Carolina at Chapel Hill Microsoft Azure Cognitive Services Research Microsoft Azure Visual Document Intelligence

We propose Universal Document processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and web-sites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark. 1 1 Code and models: https://***/microsoft/i-Code/tree/main/i-Code-Doc

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Imaginary Voice: Face-Styled Diffusion Model for Text-to-Speech

Imaginary Voice: Face-Styled Diffusion Model for Text-to-Spe...

引用

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

作者： Jiyoung Lee Joon Son Chung Soo-Whan Chung NAVER AI Lab South Korea Korea Advanced Institute of Science and Technology South Korea NAVER Cloud South Korea

The goal of this work is zero-shot text-to-speech synthesis, with speaking styles and voices learnt from facial characteristics. Inspired by the natural fact that people can imagine the voice of someone when they look at his or her face, we introduce a face-styled diffusion text-to-speech (TTS) model within a unified framework learnt from visible attributes, called Face-TTS. This is the first time that face images are used as a condition to train a TTS *** jointly train cross-model biometrics and TTS models to preserve speaker identity between face images and generated speech segments. We also propose a speaker feature binding loss to enforce the similarity of the generated and the ground truth speech segments in speaker embedding space. Since the biometric information is extracted directly from the face image, our method does not require extra fine-tuning steps to generate speech from unseen and unheard speakers. We train and evaluate the model on the LRS3 dataset, an in-the-wild audio-visual corpus containing background noise and diverse speaking styles. The project page is https://***.

关键词： image segmentation Biometrics (access control) Biological system modeling Speech enhancement Signal processing Feature extraction Acoustics

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A Water Body Extraction Network Based on Fusing SAR images and DEM Data

A Water Body Extraction Network Based on Fusing SAR Images a...

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Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Jianyu Wang Ye Tian Lei Chen Ning Wang Ningning Zhang Siao Liu Satellite Communications Branch China Telecom Co. Ltd. Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) plays a crucial role in flood monitoring and early warning systems through water body extraction techniques. However, unlike optical remote sensing images, SAR images are affected by unique imaging characteristics, including shadows, noise, and complex environments, which complicate interpretation. Most existing deep learning methods rely solely on single SAR images for model training, without incorporating additional information, leading to lower accuracy in water body interpretation, especially in challenging scenarios such as mountainous areas and densely vegetated regions. To address these challenges, we propose a water body extraction algorithm that fuses SAR images with Digital Elevation Model (DEM) data to improve the accuracy and robustness in complex environments. Four different fusion strategies are explored at both the image level and feature level, incorporating DEM and slope data into an existing encoder-decoder architecture. These enhancements effectively address the aforementioned issues. Experimental results demonstrate that the fusion approach significantly improves water body extraction precision and reduces false alarm rates compared to non-fusion methods. Through a comparative analysis of the different fusion strategies, the best approach was identified as image-level fusion using DEM data, achieving a mean Intersection over Union (mIoU) of 0.95 on the GF-Floodnet dataset.

关键词： Training Accuracy Radar imaging Feature extraction Radar polarimetry Robustness Data mining Optical sensors Synthetic aperture radar Remote sensing

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Simulation and analysis of 1024 point FFT using Axi-protocol and Xilinx IP core

Simulation and analysis of 1024 point FFT using Axi-protocol...

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International conference on Applied Electromagnetics, Signal processing and Communication (AESPC)

作者： A. Supraja Reddy B Khaleelu Rehman P. Anuradha Mohammed Abdul Nasar Mohd Ziauddin Jahnagir P. Chandra Sekhar Department of ECE Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Electrical Engineering University of Technology & Applied Sciences Nizwa Sultanate of Oman

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

This paper focuses on the implementation of a 1024-point FFT. A 1024-point FFT is significant in various applications, including signal processing, image processing, and communications, where it is essential to handle large data sets efficiently. The primary objective of this paper is to design and implement a 1024-point Fast Fourier Transform (FFT) algorithm using Verilog. This implementation aims to efficiently compute the discrete Fourier transform of a signal in a hardware environment. To design and implement the Fast Fourier Transform in Verilog module using Xilinx Vivado and to Verify the Fast Fourier Transform through MATLAB. Designing of basic 4-point and 8-point FFT in Xilinx Vivado using Verilog HDL Code and verifying the model through MATLAB by comparing. Design of 1024-point FFT in Xilinx Vivado using Verilog HDL program and verify the model through *** Synthesis of the design is carried out using Zynq-Zedboard (XC7Z020)

关键词： Fast Fourier transforms Signal processing algorithms Mathematical models Real-time systems Logic Hardware design languages MATLAB Low latency communication Field programmable gate arrays Pipeline processing

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Knowledge-Experience Graph with Denoising Autoencoder for Zero-Shot Learning in visual Cognitive Development 27th

Knowledge-Experience Graph with Denoising Autoencoder for Ze...

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27th International conference on Neural Information processing

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Dianping Grp Beijing 100190 Peoples R China

ISBN: (纸本)9783030638221;9783030638238

visual cognitive development is vital for intelligent robots to handle various types of visual tasks rather than predefined ones. It can transfer the classification ability from an original model to a novel task. However, the high reliance on large amounts of data hinders its development. The energy it costs to adjust to the novel tasks is also a tough problem. Thus we propose a model called knowledge-experience graph (KEG) to imitate the mechanisms of human brains. With the help of social knowledge stored in the knowledge graph, the novel classes can be easily added. The combination of the experience via denoising autoencoder (DAE) also takes the relationship in the visual space into account. With the propagation of information among the graph by graph convolutional network (GCN), KEG generates the classifier of the novel tasks effectively. Experiments show that KEG improves the classification accuracy of novel categories on zero-shot learning and accomplishes visual cognitive development to a certain extent.

关键词： GCN Zero-shot learning Cross-task learning Cognitive development image classification Denoising autoencoder

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Wild Animal Detection using a Machine Learning Approach and Alerting using LoRa Communication

Wild Animal Detection using a Machine Learning Approach and ...

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2022 International conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2022

作者： Bandari, Gnaneshwar Nirmala Devi, Prof. L Srividya, P. University College of Engineering Osmania University Me - Systems and Signal Processing Ece Dept. Hyderabad India University College of Engineering Osmania University Ece Department Hyderabad India University College of Engineering Osmania University Ece Department Hyderabad India

ISBN: (纸本)9781665454995

It is crucial to effectively and consistently monitor wild animals in the vicinity of the forest boundaries. In this paper, an algorithm is demonstrated to identify wild animals in order to protect them. Since there are so many different kinds of animals, manually recognizing them might be challenging. To better effectively monitor animals, this system categorizes them based on their images. Animal monitoring, theft prevention, and animal-vehicle accident prevention can all be aided by animal detection and classification. Applying efficient deep learning techniques can help with this. When an animal is detected, an alert will be sent to another device through LoRa communication because standard communications like WiFi and GSM technologies may not be available in the remote sensing area. Computer vision using Python is utilized for image processing. Raspberry Pi development board is used where the model is deployed and live streaming is processed to detect a wild animal which will differentiate between Humans and Domestic animals. Once, a wild animal is detected an alert is sent through LoRa communication to an end-user who is monitoring the wildlife. © 2022 ieee.

关键词： Convolutional neural networks

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Automatic Switching of Electric Locomotives in Neutral Sections 5

Automatic Switching of Electric Locomotives in Neutral Secti...

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5th ieee Annual International conference on Information communications Technology and Society (ICTAS)

作者： Mcineka, Christopher Thembinkosi Reddy, Serendra Durban Univ Technol Dept Elect & Comp Engn Durban South Africa

ISBN: (纸本)9781728180816

A neutral section is a phase break that is used on the Transnet freight rail system to separate the single-phase supply from the 25kV three-phase overhead traction supply. The 25kV is a stepped-down voltage from an 88kV three-phase supply coming from the national grid. Electric locomotives operate only on a single-phase supply and require a separation of supplies as the trains traverse from one phase to the other;consequently, electric locomotives are required to switch off at the neutral sections. The most common automated schemes that have been implemented in switching electric locomotives are ground, on-board, and pole switching systems;one downside to these types of schemes is their relatively large investment and infrastructure costs. This paper proposes a new automated scheme that employs the use of computer vision and image processing methods. The system uses two unique markers that are externally situated along the railway tracks and used to trigger either the opening or closing of the electric locomotive vacuum circuit breakers as the trains traverse through the neutral sections. The two markers were designed in accordance with Transnet guidelines and the system employs shape detection algorithms, including a circular Hough transform (CHT), to locate the markers, and histogram of oriented gradient (HOG) for feature extraction and training of the classifier. The model has an accuracy of approximately 72%.

关键词： Neutral section Computer Vision image processing ROI Segmentation CHT HOG features classifier

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image Augmentation for Improving Automated Eligibility-Classification for Cervical Precancer Ablation Treatment 4th

Image Augmentation for Improving Automated Eligibility-Class...

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4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Guo, Peng Xue, Zhiyun Jeronimo, Jose Gage, Julia C. Desai, Kanan T. Befano, Brian García, Francisco Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institutes RockvilleMD20850 United States Information Management Services Inc. RockvilleMD20850 United States Pima County TucsonAZ85701 United States

ISBN: (纸本)9783031070044

Cervical tissue ablation is an effective treatment approach for excising high-grade precancerous lesions, which are a direct precursor to invasive cervical cancer. However, not all women are eligible for this ablative treatment due to their cervical characteristics. In our previous study, we presented a deep learning network that used pyramidal features to determine if a cervix is eligible for ablative treatment based on visual characteristics presented in the image. Our method demonstrated promising performance and valid visualization in the task of "treatability classification". In this work, we propose using an image augmenter followed by a customized classification convolutional neural network (CNN) to overcome the challenges due to insufficient training data. We build the image augmenter using a CycleGAN model that is trained using three different datasets to ensure that the augmented images contain clinically significant morphological features. A gynecologic oncologist with more than 20 years of experience validated the augmented images. These are mixed into the set of original images to train our customized CNN. We note a performance improvement of 3.3% (to 89.8%) in treatability classification. We believe that a similar technique can also be applied to other automatic image classification applications for cervical cancer screening. © 2022, This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply.

关键词： Classification (of information)

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Improving Scene Text Recognition With A Combinative image Augmentation Approach 13

Improving Scene Text Recognition With A Combinative Image Au...

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13th International conference on Information and Communication Technology Convergence, ICTC 2022

作者： Nguyen, Ngan-Linh Lam, Gia-Huy Vo, Hoang-Thong Do, Trong-Hop Tran, Anh-Tien Cho, Sungrae University of Information Technology Faculty of Information Science and Engineering Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Scene text recognition plays an important role in various intelligent systems today such as robotic process automation and self-driving cars. These systems require knowledge of the surrounding scenery, where the words in the scene hold a lot of valuable information. For instance, scene text recognitions can serve the development of smart tourism, smart museums, and self-propelled robots. To increase the practical applicability of the solution, the recognition model needs to meet efficiently both accuracies and processing time. However, before constructing the model, most existing scene text recognition frameworks underrate the importance of a reliable and well-served augmentation stage, all the images in the dataset are often applied with common augmentation functions. In this paper, we present a combinative augmentation framework that takes random augmentation functions from different types into combinations for each individual image. In addition, the framework also randomizes the number of functions taken and every specific parameter for a particular function on the image. Thus, all these tweaks help to greatly increase the pattern diversity in images and pose an improvement in the model evaluation. Evaluated on both seen and unseen test datasets, the framework increased the accuracy by an average of 5,02% on the NRTR model and 2.36% on the VietOCR model. © 2022 ieee.

关键词： image enhancement

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