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PDCE: Patch-wise Dynamic Curve Estimation for Low-Light image Enhancement

PDCE: Patch-wise Dynamic Curve Estimation for Low-Light Imag...

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

作者： Ruiyuan Chen Zhixin Li Han Zeng Yifan Liu Tao He Tiecheng Song School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement (LLIE) can be reformulated as an image-specific curve estimation (CE) problem. Traditional CE-based methods struggle with issues such as uniform processing across different regions, static parameter estimation, and lack of effective global semantic enhancement. To address these limitations, we propose a novel unsupervised learning framework, Patch-wise Dynamic Curve Estimation (PDCE), which dynamically adjusts and optimizes enhancement curves according to local patch brightness and the iteration process. Specifically, we present a Vision-Language Curve Discriminator (VLCD), which dynamically determines the curve type for each patch, avoiding uniformly applying the curve on the whole image. We introduce a Curve Parameter Estimator (CPE), which dynamically updates curve parameters and adjusts enhancement effects based on the output of the previous iteration. Furthermore, we design a visual State Space-based Semantic Enhancement Module (VSEM), which captures global receptive fields and enriches semantic features through the Mamba-based U-Net architecture. Extensive experimental results show the superiority of our PDCE over state-of-the-art methods for LLIE.

关键词： visualization Parameter estimation Semantics Estimation Speech enhancement Signal processing Iterative methods Unsupervised learning image enhancement Optimization

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An Analytical Study of Hyperparameters and Architectural Impacts for image Super-Resolution

An Analytical Study of Hyperparameters and Architectural Imp...

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Signal processing, communications and Engineering Management conference (ISPCEM), International

作者： Shengxiao Gong School of Engineering and Computing University of Central Lancashire Preston The United Kingdom

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Super-resolution is particularly helpful for applications like larger computer screens, portable electronics like cellphones and cameras, and high-definition television sets. It plays a crucial role in improving visual perception by transforming low-resolution images into clearer, high-resolution ones. The integration of deep learning methodologies has demonstrated significant promise in mitigating the constraints of conventional approaches. Three traditional deep learning networks are used in this experiment to perform picture super-resolution tasks. This experiment uses the well-known Very Deep Super-Resolution (VDSR) network as a reference and evaluates the effects of different hyperparameters on the results. The experiment then presents the Convolutional Neural Network with Recurrent Neural Network (CNN+RNN) and Generative Adversarial Network (GAN). The benefits of these various network topologies are contrasted, and the differences and enhancements provided by each configuration are examined. Lastly, subjective comparison graphs are provided and objective measurements are used to assess the outcomes. The quality of the experimental results is directly impacted by the network structure's complexity.

关键词： Training Deep learning TV Recurrent neural networks Superresolution Generative adversarial networks Data models Convolutional neural networks Signal resolution visual perception

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Spatial Global Context Attention for Convolutional Neural Networks: An Efficient Method

Spatial Global Context Attention for Convolutional Neural Ne...

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

作者： Yang Yu Yi Zhang Xingxing Zhu Zeyu Cheng Zhe Song Chengkai Tang School of Electronics and Information Northwestern Polytechnical University Xi'an China Aeronautics Computing Technology Research Institute Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Capturing global contextual information within an image can significantly enhance visual understanding. However, current attention methods model long-range dependencies between features by aggregating query-specific global context to each query position. These methods are inefficient and consume a huge amount of memory and computational resources, making them less practical. To address this issue, we propose a simple, low-cost, and high-performance Spatial Global Context Attention (SGCA) module. This module aggregates query-independent global context to update features at each query position, capturing spatial global contextual information in an efficient and effective manner, significantly improving feature representations, which contributes to more precise classification results. The proposed SGCA is lightweight and flexible, making it suitable as an independent add-on component that can be applied to various convolutional neural networks (CNNs) to create a family of new architectures named SGCANet. Without bells and whistles, extensive experimental results on CIFAR-100 and imageNet-1K for image recognition tasks demonstrate that our method significantly outperforms other counterparts in classification performance at a cheaper cost, achieving leading results.

关键词： visualization image recognition Costs Computational modeling Memory management Semantics Signal processing Computational efficiency Convolutional neural networks Context modeling

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Multi-Channel Adaptive Partitioning Network for Block-Based image Compressive Sensing

Multi-Channel Adaptive Partitioning Network for Block-Based ...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Hui, Chen Liu, Shaohui Jiang, Feng School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

image compressive sensing (CS) technology has attracted increasing attentions in the past few years, and a great deal deep learning-based methods have been proposed. However, the existing methods use fixed-scale blocks for sampling and re-construction. Such practice will inevitably result in the in-ability to distinguish between significant regions and background regions, and even waste excessive sampling resources on the background ones to a large extent. In this paper, we propose a novel multi-channel adaptive partitioning network for block-based image CS, in which image blocks of different scales are utilized to distinguish regions of different saliency. Specifically, an adaptive block partitioning method based on image saliency is put forward, using which significant regions are divided into large blocks and background regions are divided into small blocks. Subsequently, blocks of different scales are fed to different-channel networks for sampling to yield the compressed measurements. To improve the re-construction quality of the image, a scalable multi-scale re-construction network is proposed to recover the compressed measurements into the reconstructed image. Experimental results compared with the state-of-the-art show that the proposed scheme achieves significant improvements in terms of objective metrics and subjective visual image quality. © 2022 IEEE.

关键词： image compression

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A Computer Vision and Vibrohaptic Glove-Based Piano Learning System for the visually Impaired

A Computer Vision and Vibrohaptic Glove-Based Piano Learning...

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International conference on Advanced Communication Technology (ICACT)

作者： Ian Juha Cho Jin Park Hosung Bae Hankuk Academy of Foreign Studies Yongin South Korea

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

The visually impaired are unable to enjoy leisure activities as much as ordinary people due to various limitations. To expand the scope of leisure activities for the visually impaired, we have developed a vibration glove-based system that helps with piano learning. Previous research used 88 infrared light-emitting diodes and gloves with infrared receivers to provide feedback to the user, but this method had many limitations. In particular, the inconvenient user experience and low accuracy were the biggest problems. Our method solves both problems using a camera and an image processing algorithm. As a result of testing the model on 20 piano images, it was shown that all keys were perfectly recognized in 75% of cases, and the gloves could be comfortably used in practice without any difficulty. Thus, our method presents a simpler user experience for the visually impaired, without requiring any special modifications to the piano.

关键词： Vibrations Learning systems Computer vision image recognition visual impairment Receivers Light emitting diodes User experience communications technology Testing

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A Multi-Stage Spike Stream processing and image Reconstruction Method for Industrial Applications

A Multi-Stage Spike Stream Processing and Image Reconstructi...

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IEEE International conference on Trust, Security and Privacy in Computing and communications (TrustCom)

作者： Shuaipeng Wu Changhao Yuan Kejiang Ye Southern University of Science and Technology Chinese Academy of Sciences Shenzhen Institute of Advanced Technology University of Chinese Academy of Sciences

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the advancement of new industrialization, production processes or scenarios characterized by automation and intelligence require monitoring by more powerful visual inspection tools to maintain production continuity, safety, and efficiency. Spike vision sensors, with their high temporal resolution and high dynamic range, can capture subtle changes of high-speed moving objects in complex industrial environments, providing precise data support for equipment maintenance, fault detection, and condition monitoring. However, in industrial scenarios, factors such as mechanical vibrations, electromagnetic interference, and extreme strong light affect the quality of sensor data, leading to higher noise levels and increased probability of data loss and errors. To address this, we propose a multi-stage processing method for spike data processing and image reconstruction. It first performs preliminary feature extraction and denoising through an anti-noise feature encoder. Building on this, it integrates features and enhances consistency through spatiotemporal information utilization. Finally, it reconstructs the images through an fine-tuned spike reconstruction model. Experimental results show that our method can effectively reduce noise and improve data quality, achieving good performance on industrial datasets. This provides higher quality data support for downstream tasks such as predictive maintenance and high-speed production inspection in industrial scenarios.

关键词： visualization Noise reduction Noise Production Streaming media Inspection Vision sensors Feature extraction Data processing image reconstruction

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Automated Quality Assessment of Mangosteen Fruits Using Multimodal Deep Learning

Automated Quality Assessment of Mangosteen Fruits Using Mult...

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2023 IEEE Global conference on Information Technologies and communications, GCITC 2023

作者： Dalal, Yash M Amuthabala, K. Patil, Shantala Devi Reva University School of Cse India

ISBN: (纸本)9798350308167

In this groundbreaking take a look at, we recommend an revolutionary method for Mangosteen fruit exceptional evaluation the use of a hybrid CNN-RNN model. Our method leverages multimodal deep mastering techniques, integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) to efficiently analyze high-decision Mangosteen fruit pix. The dataset incorporates over one thousand various photos sourced from Kaggle, cautiously segmented into Training, Validation, and Testing Sets, ensuring sturdy model learning. Through high-quality-tuning hyperparameters with the Validation Set, we optimized version overall performance with out overfitting risks. The impartial assessment on the Testing Set demonstrates our model's splendid capability to generate exact and degree-specific captions for Mangosteen fruit snap shots, appropriately figuring out unique levels. Rigorous evaluation of the model's schooling performance the use of container loss, elegance loss, and object loss components suggests dependable item localization and relevant caption technology. The imply Average Precision (mAP) graph exhibits growing accuracy, indicating non-stop model improvement at some stage in training. Our integrated CNN-RNN version gives informative captions for Mangosteen fruit photos, marking a widespread development in automating fruit quality assessment. By bridging modern AI technologies with actual-world agricultural packages, our studies guarantees a extra sustainable and green destiny for agricultural practices. The accurate identification of various stages of Mangosteen end result allows knowledgeable choice-making, main to stepped forward manufacturing and the shipping of top class-great produce to fulfill consumer demands. © 2023 IEEE.

关键词： Automated assessment Comprehensive analysis Convolutional neural networks Deep learning Fruit quality image processing Machine learning Mangosteen fruits Multimodal fusion Production optimization Spectral data visual images

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Smart Saliency Detection for Prosthetic Vision

Smart Saliency Detection for Prosthetic Vision

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Workshop held at the 12th International conference on Model and Data Engineering, MEDI 2023

作者： Khalifa, Nermin Al-Atabany, Walid Selim, Sahar School of Information Technology and Computer Science Nile University Giza12677 Egypt Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza12677 Egypt

ISBN: (纸本)9783031557286

People with visual impairments often have difficulty locating misplaced objects. This can be a major barrier to their independence and quality of life. Retinal prostheses can restore some vision to people with severe vision loss. We introduce a novel real-time system for locating any misplaced objects for people with visual impairments using retinal prostheses. The system combines One For All (OFA) for visual Grounding and Google Speech Recognition to identify the object to be located. It then uses an image processing technique called grabCut to extract the object from the background to emphasize the located object and incorporate it with the background frame to send it to the retinal implant for stimulation. The system was evaluated on a set of images containing misplaced objects and was able to locate the objects in real-time successfully. We believe that this system could be used to improve the independence and quality of life of people with visual impairments. The system is still in the early stages of development, but we are working to improve its performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： prosthetic vision simulated prosthetic vision vision augmentation vision language models visual grounding

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Weighted Robinson Compass Gradient and Charbonnier Penalty Function as a Loss Function 7

Weighted Robinson Compass Gradient and Charbonnier Penalty F...

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7th IEEE International conference on Recent Advances and Innovations in Engineering, ICRAIE 2022

作者： Rachna, U. Dhruv Shindhe, S. Omkar, S.N. PES University Department of Electronics and Communications Bangalore India Indian Institute of Science ARTPARK Bangalore India Indian Institute of Science Department of Aerospace Bangalore India

ISBN: (纸本)9781665489102

Loss function quantifies how well a machine learning model is performing, it computes the difference between the ground truth and the reconstructed outcomes. In image processing tasks such as image dehazing, image deblurring and image denoising the most popular loss functions used are Mean Squared error(MSE) and perceptual loss. MSE is less computationally expensive but fails to recreate precise boundaries whereas perceptual loss is more computationally expensive as they use feature vectors from a pre-trained model like VGG-net. In smaller on the edge systems like autonomous navigation systems, one of the most critical tasks is relying on visual cues, the take in the video feed from the environment and make decision on it after a few prepossessing steps to eliminate things like physical barriers, blurr, and smoke that clutter a robot's environment are one of the several factors that affect autonomous navigation. We need systems that aren't computationally expensive but also have a good reconstruction. For this we propose a loss function that incorporates gradients along with MSE for faster and better reconstruction. © 2022 IEEE.

关键词： Smoke

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Deep Dictionary Learning with an Intra-Class Constraint

Deep Dictionary Learning with an Intra-Class Constraint

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Yuan, Xia Gou, Jianping Yu, Baosheng Yu, Jiali Yi, Zhang School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science The University of Sydney DarlingtonNSW Australia School of Mathematical Sciences University of Electronic Science and Technology of China China School of Computer Science Sichuan University Chengdu China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for represen-tation learning and visual recognition. However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information. To make full use of the category information of different samples, we pro-pose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specif-ically, we design the intra-class compactness constraint on the intermediate representation at different levels to encour-age the intra-class representations to be closer to each other, and eventually the learned representation becomes more dis-criminative. Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experi-mental results on four image datasets show that our method is superior to the state-of-the-art methods. © 2022 IEEE.

关键词： image classification

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