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Super-Resolving SAR images with Diffusion Models: A Dual Evaluation of Metrics and Applications

Super-Resolving SAR Images with Diffusion Models: A Dual Eva...

引用

International conference on Signal processing Proceedings (ICSP)

作者： Zilong Chen Siqian Zhang Boli Xiong College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

SAR, recognized for its capability to capture images under various weather conditions and at any time, is served as a key technology in remote sensing domains. Nonetheless, the inherent limitations of imaging systems often result in SAR images that do not achieve high resolution, thereby constraining their specific applications. Drawing inspiration from the efficacy of diffusion models in generative tasks involving natural images, we have devised a model that converts Gaussian noise into a super-resolution image derived from low-resolution input, utilizing a Markov chain framework and integrating residual prediction to facilitate faster convergence. The performance of this model is assessed in comparison to leading methodologies, including Generative Adversarial Networks and Transformers, employing both traditional evaluation metrics and semantic segmentation tasks. Experimental findings derived from the MSTAR and SARBake datasets reveal that the super-resolution algorithm utilizing a diffusion model exhibits superior performance relative to alternative models, as evidenced by conventional metrics. This algorithm not only demonstrates exceptional visual quality but also proves effective in downstream applications, underscoring its considerable potential for tasks involving super-resolution of SAR images.

关键词： Measurement visualization Semantic segmentation Superresolution Signal processing algorithms Diffusion models Transformers Radar polarimetry Signal resolution Remote sensing

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Cartoon image processing and promotion strategy based on AI intelligent technology 2

Cartoon image processing and promotion strategy based on AI ...

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2nd International conference on Information Science and Education, ICISE-IE 2021

作者： Yujie, Bai School of Literature and Law Wuhan DongHu University Hubei Wuhan China

ISBN: (纸本)9781665438292

This paper mainly explores cartoon image processi ng from the perspective of intelligent technology. It takes cartoon image shaping as the starting point to construct cartoon image int elligent database. On this basis, this paper proposes an AI intellig ent cartoon image processing method. The algorithm concept of t his technology is novel and the process is clear. It can intelligently identify the physical image and shape the three-dimensional seen e of cartoon image. Using intelligent technology to process inform ation materials can improve the high effectiveness and influence o f cartoon images. This paper focuses on the common characteristi cs of brands and cartoon images to discuss the compatibility of br ands and cartoon images, and further promote the development o f brand vision. © 2021 ieee

关键词： image enhancement

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SWT-UNet: Medical image Segmentation Based on Multi-modality UNet with Sliding Window Transformer Block

SWT-UNet: Medical Image Segmentation Based on Multi-modality...

引用

ieee International conference on Bioinformatics and Biomedicine (BIBM)

作者： Yangqianhui Zhang Dong Han Xiaoming Gang Jiajun Ma Xu Yuan School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Anshan Municipal Central Hospital Anshan China Shenhua Hollysys Information Technology Co. Ltd Beijing China School of Software Technology Dalian University of Technology Dalian China

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we conduct research on multi-modal medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks and Vision Transformer. This paper proposes a multi-modal medical image segmentation model SWT-UNet based on the CNN-ViT hybrid framework. The self-attention mechanism and sliding window design of the visual Transformer are used to capture the global feature association and break the limitation of the receptive field caused by the inductive bias of the convolution operation. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size, so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on two multi-modal medical image datasets validate that the algorithm can achieve efficient medical image segmentation at a lightweight scale.

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Facial Expression Recognition Using a Simplified Convolutional Neural Network Model 4

Facial Expression Recognition Using a Simplified Convolution...

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4th International conference on communications, Signal processing, and their Applications, ICCSPA 2020

作者： Kandeel, Amany Rahmanian, Mina Zulkernine, Farhana Abbas, Hazem M. Hassanein, Hossam School of Computing Queen’s University

ISBN: (纸本)9781728165356

Facial Expression Recognition (FER) is one of the most important information channels by which Human-Computer Interaction (HCI) systems can recognize human emotions. The importance of FER is not limited to the direct interaction between the machine and humans but can be extended to security, virtual reality, education, and entertainment. In this paper, we propose two Convolutional Neural Network (CNN) models for FER. One of these models achieved 100% accuracy for the JAFFE and CK+ benchmark datasets with lower computational complexity. We applied image augmentation techniques and image enhancement techniques with the first m odel. T he o ther C NN m odel i s a n e xtended version of the first m odel t hat h as b een v alidated f or t he more challenging FER2013 dataset and we obtained 69.32% for this dataset. By comparing to the recent state-of-the-art approaches to FER, we demonstrate the superior accuracy and efficiency o f t he p roposed approaches. © 2020 ieee.

关键词： image enhancement

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Modulating Bottom-Up and Top-Down visual processing via Language-Conditional Filters

Modulating Bottom-Up and Top-Down Visual Processing via Lang...

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ieee Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ilker Kesen Ozan Arkan Can Erkut Erdem Aykut Erdem Deniz Yü ret KUIS AI Center Ko&#x00E7 University Computer Engineering Department Ko&#x00E7 Amazon Search Computer Engineering Department Hacettepe University

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual attention over high-level visual features, may not be optimal. We hypothesize that the use of language to also condition the bottom-up processing from pixels to high-level features can provide benefits to the overall performance. To support our claim, we propose a U-Net-based model and perform experiments on two language-vision dense-prediction tasks: referring expression segmentation and language-guided image colorization. We compare results where either one or both of the top-down and bottom-up visual branches are conditioned on language. Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance. Our linguistic analysis suggests that bottom-up conditioning improves segmentation of objects especially when input text refers to low-level visual concepts. Code is available at https://***/ilkerkesen/bvpr.

关键词： visualization image segmentation image color analysis Grounding Computational modeling Process control Predictive models

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Design of RNS-KSA Based 2D FIR Filter

Design of RNS-KSA Based 2D FIR Filter

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), ieee International conference on

作者： Raghavendra Reddy B. Rajesh A. Nandini Prem Kumar K. S. Chakradhar Dept. of Electronics and Communications Engineering (ECE) Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India School of Engineering Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Two-dimensional finite impulse response filters have the benefits of phase linearity and assured stability, making them appealing in applications. 2D Finite Impulse Response (FIR) filters serve a significant role in different applications, such as image processing, medical field, pattern recognition, robot vision, and seismic or geophysical signal processing. This paper presents a novel RNS-based 2D Finite Impulse Response (FIR) filter using Residue Number System (RNS) and floating-point arithmetic operations, in which Residue Number System (RNS) provides substantially lower ranges of numbers in arithmetic computations than starting numbers and Floating-point arithmetic provides more accuracy and dynamic range than fixed-point arithmetic. The Floating Point Adder in the filter is designed using a Kogge-Stone parallel prefix adder (KSA) to improve addition speed. The proposed novel 2D FIR filter can process 32, 48, and 64-bit data by just changing the corresponding RNS moduli selection which makes it a better choice over a fixed-point-based 2D FIR filter. This filter is designed in Verilog HDL for kernel sizes 3X3, 5X5 and 7X7 and synthesized using Altera's Quartus software for FPGA Board Cyclone III (EP3C16F484C6) to evaluate the design parameters of the filter.

关键词： Finite impulse response filters image processing Dynamic range Information filters Software Pattern recognition Hardware design languages

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A System Architecture in Multiple Views for an image processing Graphical User Interface 23

A System Architecture in Multiple Views for an Image Process...

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23rd International conference on Enterprise Information Systems, ICEIS 2021

作者： Maciel, Roberto Wagner Santos Soares, Michel S. Dantas, Daniel Oliveira Departamento de Computação Universidade Federal de Sergipe SE São Cristóvão Brazil

ISBN: (纸本)9789897585098

Medical images are important components in modern health institutions, used mainly as a diagnostic support tool to improve the quality of patient care. Researchers and software developers have difficulty when building solutions for segmenting, filtering and visualizing medical images due to the learning curve, complexity of installation and use of image processing tools. VisionGL is an open source library that facilitates programming through the automatic generation of C++ wrapper code. The wrapper code is responsible for calling parallel image processing functions or shaders on CPUs using OpenCL, and on GPUs using OpenCL, GLSL and CUDA. An extension to support distributed processing, named VGLGUI, involves the creation of a client with a workflow editor and a server, capable of executing that workflow. This article presents a description of architecture in multiple views, using the architectural standard ISO/IEC/ieee 42010:2011, the 4+1 View Model of Software Architecture and the Unified Modeling Language (UML), for a visual programming language with parallel and distributed processing capabilities. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Diagnosis

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An improved GAN-based approach for image inpainting 15

An improved GAN-based approach for image inpainting

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15th RIVF International conference on Computing and Communication Technologies (RIVF)

作者： Ngoc-Thao Nguyen Bang-Dang Pham Thanh-Sang Thai Minh-Thanh Nguyen Univ Science Adv Program Comp Sci Ho Chi Minh City Vietnam Univ Science Fac Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9781665404358

image inpainting aims to complete missing regions in images, effectively serves imagery processes like historical image restoration or photo editing. This task is challenging because the completion should maintain visual coherence throughout the image. This paper's contribution lies in an architecture that comprises multiple generators and discriminators to achieve better inpainting results. The two generators work sequentially, in which the first model coarsely reconstructs the missing regions, and the latter completes these regions following the given prior knowledge. Meanwhile, the discriminator stage includes two parallel, global and local branches, allowing for more significant discrimination. We further suggest using dilated convolution, which effectively broadens the receptive field, and WGAN-GP to mitigate gradient vanishing. Both quantitative and qualitative experiments on standard datasets have shown that our method provides more plausible results than current baselines.

关键词： visualization Convolution Computational modeling Computer architecture Generators communications technology image restoration

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PERCEPTNET: A HUMAN visual SYSTEM INSPIRED NEURAL NETWORK FOR ESTIMATING PERCEPTUAL DISTANCE

PERCEPTNET: A HUMAN VISUAL SYSTEM INSPIRED NEURAL NETWORK FO...

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ieee International conference on image processing (ICIP)

作者： Hepburn, Alexander Laparra, Valero Malo, Jesus McConville, Ryan Santos-Rodriguez, Raul Univ Bristol Dept Engn Math Bristol Avon England Univ Valencia Image Proc Lab Valencia Spain

ISBN: (纸本)9781728163956

Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was usually taken from the human visual perceptual system and how the system processes different perturbations in order to replicate to what extent it determines our ability to judge image quality. While recent works have presented deep neural networks trained to predict human perceptual quality, very few borrow any intuitions from the human visual system. To address this, we present PerceptNet, a convolutional neural network where the architecture has been chosen to reflect the structure and various stages in the human visual system. We evaluate PerceptNet on various traditional perception datasets and note strong performance on a number of them as compared with traditional image quality metrics. We also show that including a nonlinearity inspired by the human visual system in classical deep neural networks architectures can increase their ability to judge perceptual similarity. Compared to similar deep learning methods, the performance is similar, although our network has a number of parameters that is several orders of magnitude less.

关键词： perceptual distance human visual system neural networks

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Robust Structure-Aware Low-Light Enhancement via a Latent Low-Rank Embedding Retinex Model 7

Robust Structure-Aware Low-Light Enhancement via a Latent Lo...

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7th International conference on Computational Intelligence and Applications, ICCIA 2022

作者： Wu, Mingna Yang, Xudong Feng, Dong Li, Zhigang Zhang, Long Dong, Hao Wang, Shu Anhui Institute of Optics and Fine Mechanics Hefei Institute of Physical Science Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China Institute of Material Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781665495844

Robustness to noise is a significant issue in the field of low-light image enhancement (LLIE). Most existing LLIE methods, which ignore the intensive noise contained in images or use a pre/postprocessing step to remove noise, fail to achieve pleasant visual effects in practical tasks. To address this problem, in this paper, based on Retinex theory, we propose jointly integrating low-light enhancement and denoising into a unified framework with latent low-rank embedding. Specifically, an 2,1-norm term is first introduced to measure the reconstruction error and enhance the robustness of decomposed components to various types of noise. Then, a smooth prior is applied to the illumination component, and a structure-aware gradient fidelity term and latent low-rank embedding are deployed to resist noise and retain the texture of the reflectance component. Considering the expensive time cost of low-rank processing, we design an accelerated low-rank approximation strategy without losing structural information. Afterward, the decomposed components are sequentially estimated, avoiding noise from the other component. Finally, the enhanced images are generated by combining the estimated reflectance with the illumination adjusted based on gamma correction. Extensive experiments demonstrate that our method performs significantly better than several state-of-the-art methods in terms of enhancing and denoising on three public datasets. © 2022 ieee.

关键词： image enhancement

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