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Visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping 5

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

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5th ieee international conference on image processing applications and Systems (IPAS)

作者： Gabr, Mohamed Younis, Hana Ibrahim, Marwa Alajmy, Sara Alexan, Wassim German Univ Cairo Fac MET Cairo Egypt German Univ Cairo Fac IET Cairo Egypt

ISBN: (纸本)9781665462198

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Chaotic maps cryptography image encryption Mersenne Twister S-box Tent map

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Enhancing Accuracy in Doppler Frequency Shift Estimation and Velocity Measurement for Doppler Velocity Log applications: A Comparative Study 11

Enhancing Accuracy in Doppler Frequency Shift Estimation and...

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11th ieee international conference on signal processing and Integrated Networks, SPIN 2024

作者： Annapurna, S.M. Sarath Chandran, R. Haneesh Sankar, T.P. Rajesh, K.R. CDAC Thiruvananthapuram India

ISBN: (纸本)9798350308433

This research examines the prominent role of sonar technology, especially the Doppler Velocity Log (DVL), in the determination of ship velocity. The DVL employs a four-beam Janus configuration to measure a ship's velocity relative to the waterbed by transmitting acoustic pulses along different azimuth-oriented beams. The study focuses on a scenario where a ship moving through a waterbed produces a Doppler frequency shift, a critical parameter for determining the ship velocity. This paper presents a thorough comparison between the correlation-based broad-band pulse-pair method and the narrow-band least squares method for the determination of Doppler frequency shift. The selection of these methods dynamically adapts to varying water depths. The algorithms are developed and simulated in MATLAB, followed by implementation in Digital signal Processor (DSP), and further validated through trials in diverse water bodies. Results indicate the broad-band pulse-pair method excelling in low-depth scenarios, while the narrow-band least squares algorithm performs optimally in higher depths. Dynamic algorithm switching based on the water depth exhibits an adaptive approach, enhancing the accuracy of Doppler shift estimations. This study contributes valuable insights for optimizing DVL frequency estimation, providing a practical solution for underwater navigation across varying depth conditions. © 2024 ieee.

关键词： Ships

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Multi-Layer Visual Perception for No-Reference image Quality Assessment 6

Multi-Layer Visual Perception for No-Reference Image Quality...

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6th ieee international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Wang, Yingzhen Wang, Qingchun Li, Yingsong College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China

ISBN: (纸本)9798350399059

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results. © 2023 ieee.

关键词： Convolutional neural networks

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Refactoring ISP for High-Level Vision Tasks 39

Refactoring ISP for High-Level Vision Tasks

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ieee international conference on Robotics and Automation (ICRA)

作者： Shi, Yongjie Li, Songjiang Jia, Xu Liu, Jianzhuang Peking Univ Key Lab Machine Percept Beijing 100871 Peoples R China Huawei Technol Noahs Ark Lab Shenzhen 518129 Peoples R China Dalian Univ Technol Sch Artificial Intelligence Dalian 116024 Peoples R China

ISBN: (纸本)9781728196817

The image signal processing (ISP) pipeline, which transforms raw sensor measurement to a color image, is composed of a sequence of processing modules. Traditionally, the ISP pipeline is manually tuned by experts for human perception. The resulting handcrafted ISP configuration does not necessarily benefit the downstream high-level vision tasks. To mitigate these problems, this paper presents a simple yet effective framework based on Evolutionary Algorithm to search for a set of compact ISP configurations for high-level vision tasks. In particular, we encode ISP structure into a binary string and ISP parameters into a set of float numbers. Then we jointly optimize them with task-specific loss and ISP computation budgets (e.g., running time) through solving a nonlinear multi-objective optimization problem. By mutating the configurations of the ISP pipeline, we are able to remove redundant modules and design an ISP with both low cost and high accuracy. We validate the proposed method on extreme noisy and low-light raw images, and experimental results show that our framework can help find effective and efficient ISP configurations for both object detection and semantic segmentation tasks. We further provide a detailed analysis on the importance of different modules in the ISP configurations, which benefits the design of ISP for downstream tasks in the future.

关键词： image segmentation Pipelines Semantics Object detection Transforms signal processing Search problems

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A Multi-Scale Feature Aggregation Based Lightweight Network for Audio-Visual Speech Enhancement 48

A Multi-Scale Feature Aggregation Based Lightweight Network ...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xu, Haitao Wei, Liangfa Zhang, Jie Yang, Jianming Wang, Yannan Gao, Tian Fang, Xin Dai, Lirong NERC-SLIP Hefei China Machine Learning Group Ethereal Audio Lab Shenzhen China Tsinghua Shenzhen International Graduate School Shenzhen China

ISBN: (纸本)9781728163277

Audio-visual speech enhancement (AVSE) was shown to be superior over conventional audio-only counterpart for improving the speech quality. However, most existing AVSE models are heavyweight in the sense of parameter count, which is inappropriate for the deployment and practical applications. In this paper, we therefore present a lightweight AVSE approach (called M3Net) by incorporating several multi-modality, multi-scale and multi-branch strategies. Three multi-scale techniques are designed for the visual and audio streams, including multi-scale average pooling (MSAP), multi-scale ResNet (MSResNet) and multi-scale short time Fourier transform (MSSTFT). It is shown that each multi-scale module positively contributes to the performance. Also, we consider four skip connections for the audio-visual feature aggregation, which have a great complementary effect on the designed multi-scale techniques. Experimental results show that these techniques are flexible in combination with existing approaches, and more importantly obtain a comparable performance with a smaller model size compared to the heavyweight networks. © 2023 ieee.

关键词： Speech enhancement

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Ancient Indian Murals Digital Restoration through image InPainting 10

Ancient Indian Murals Digital Restoration through Image InPa...

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10th international conference on signal processing and Integrated Networks, SPIN 2023

作者： Singh, Usneek Maiti, Somsukla Saini, Anil Dhiraj Birla Institute of Technology and Science Pilani India CSIR-CEERI Pilani India

ISBN: (纸本)9781665490993

Murals and paintings are an integral part of the history of any civilization. India has deep roots in ancient history and possesses some of its most valuable assets in ancient murals dating back several hundred years. They feature Hindu mythology, traditions, incidents of bravery and cultural events and thus have a substantial social significance in human society. Unfortunately, some of these assets lose value over time due to environmental factors and human negligence. The ground truth regarding their original colour, textures and painting style references is not currently available, due to which their physical restoration is not possible. Therefore, the only way to restore them is through their digital counterparts and image inpainting techniques. In this paper, a complete end-to-end image restoration framework has been presented to virtually repair the damaged regions of the mural paintings using a selective neural style technique for the transfer of realistic-looking damaged textures, a modified U-Net for detecting the damaged regions in an image and a Partial Convolution network for effective image inpainting. The photographs of Rajasthani paintings scrapped from online sources are used as the dataset. The restored results based on the perceptual quality of completion on ancient Indian murals are promising;thus, they may contribute to the digital restoration of murals. © 2023 ieee.

关键词： Convolution

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Classification of 5G Spectrum Sensing signals Based on Visual Language Models 15

Classification of 5G Spectrum Sensing Signals Based on Visua...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohan, Bhuma Chandra Challa, Naga Raju Babu, M. Lenin Bapatla Engineering College Department of Ece Bapatla India

ISBN: (纸本)9798350370249

The availability of spectrum for wireless communication has become a precious and costly resource in recent times due to the congestion of the spectrum with multiple applications. Hence, spectrum sensing can facilitate the optimal utilization of the spectrum paving the way for reliable communication. In this work, a spectrum sensing algorithm using a deep learning approach is presented. In recent times, deeper architectures trained on massive datasets are available and suitable for various scenarios such as image classification, object detection, image segmentation, and visual question and answer. Received signals are usually contaminated with noise and it is difficult to differentiate between the signals and noise at low SNRs. One-dimensional data is converted to two-dimensional images (spectrograms) and the image embeddings are obtained from the deeper architectures. The spectrum sensing problem is mapped as an image classification problem in this scenario. The deeper architectures used in this paper are Contrastive Language image Pre-trained models. The problem of identifying whether the received is a signal or noise is treated as a binary classification problem. Suitable metrics i.e., classification accuracy, precision, recall, and F1 score are used for assessing the proposed algorithm. Cross-validations are also carried out to ensure the robustness of the proposed model. Even at low SNRs of -20 dB a classification accuracy of well above 84% is obtained on the Filter Bank Multi Carrier 5G data set. © 2024 ieee.

关键词： 5G data set Contrastive language image Model Filter Bank Multi-Carrier signal-to-Noise Spectrum Sensing

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Fusing Depths: Investigating the Synergy of Convolutional Neural Networks and Long Short-Term Memory Networks for Enhanced image Caption Generation 1

Fusing Depths: Investigating the Synergy of Convolutional Ne...

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1st ieee international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Sharma, Vikas Alekh Chaudhary, Kirti Vashishth, Tarun Kumar Chaudhary, Sachin Kumar, Bhupendra Iimt University School of Computer Science and Applications U.P Meerut India Delhi Ncr Ghaziabad India Gl Bajaj Institute of Management Greater Noida India

ISBN: (纸本)9798350372748

Generation, poses a challenge due to the intricate visual content and nuanced semantic details. This research introduces a novel approach for image captioning by seamlessly integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed methodology involves leveraging CNNs to extract visual features and employing LSTM for the generation of descriptive captions. To further enhance the model's performance, attention mechanisms are incorporated, allowing the model to focus on relevant visual features during the caption generation process. The evaluation of our modified model utilizes standard benchmark datasets, such as Flickr8k, and employs metrics including CIDEr, METEOR, and BLEU scores. Through rigorous assessment, our model showcases improved performance, demonstrating its efficacy in comparison to existing methods. The versatility of the proposed system extends its potential applications to image retrieval, image description, and other multimedia scenarios requiring robust image analysis and natural language processing capabilities. This research contributes to the advancement of image captioning techniques, offering a promising solution for real-world applications in multimedia and artificial intelligence domains. The goal of this study is to explore and leverage the combined capabilities of CNNs and LSTMs to enhance the process of generating descriptive captions for images. By merging the strengths of CNNs in image feature extraction with the sequential understanding and context modeling abilities of LSTMs, the aim is to develop a more sophisticated and effective approach for generating accurate and contextually relevant captions that better capture the nuances and details of the images. This research seeks to push the boundaries of image captioning technology, ultimately improving the quality and richness of generated captions, and advancing the state-of-the-art in artificial intelligence and computer vision applicatio

关键词： Long short-term memory

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

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ieee international conference on Visual Communications and image processing (VCIP)

作者： Zhang, Yichi Liu, Hengyu Ding, Dandan Ma, Zhan Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665475921

Compared to RGB images non-linearly mapped from RAW data through the image signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Computational photography low-light transformer fast Fourier convolution

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FPGA-Compatible Fog Rectification and Contrast Enhancement for Improved Vision Algorithms in Foggy Environments 4

FPGA-Compatible Fog Rectification and Contrast Enhancement f...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Siddiqui, Khadim Moin Chowdhary, Anjali Yadav, Rani Yadav, Karandeep Verma, Sejal Singh, Anurag Kanaujia, Surabhi Tripathi, Shiv Mani Singh, Beer Gautam, Abhinav K. S.R. Institute of Management & Technology Electrical & Electronics Engineering Lucknow India S.R. Institute of Management & Technology Electronics & Communication Engineering Lucknow India S.R. Institute of Management & Technology Electronics & Communication Engineering Department Lucknow India

ISBN: (纸本)9798350349900

Fog rectification is a crucial preprocessing step in enhancing image quality for applications like autonomous driving and object recognition, particularly in foggy and hazy conditions. This paper presents a streaming fixed-point implementation of a fog rectification and contrast enhancement algorithm tailored for FPGA deployment aiming to enhance visibility and contrast in foggy images. The fog rectification involves the dark channel of the image calculating the airlight map, refining it with filters and restoring the defogged image. Contrast enhancement is achieved through RGB to gray conversion and contrast stretching. The proposed algorithm is designed to efficiently process foggy RGB images, removing haze and enhancing contrast. The implementation utilizes fixed-point blocks from Vision HDL Toolbox for FPGA compatibility and employs a streaming pixel format for real-time processing. Simulation results demonstrate the effectiveness of the algorithm in improving image quality for vision algorithms in foggy environments. © 2024 ieee.

关键词： image quality

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