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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ma, Xinchen Lu, Yue School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloemxxxxc/TRIFORMER. © 2025 IEEE.

关键词： image restoration transformer Ultra-high-definition(UHD)

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ROTATED R-CNN: A TWO-STAGE OBJECT DETECTION METHOD ADAPTED TO ORIENTED BOUNDING BOXES 31

ROTATED R-CNN: A TWO-STAGE OBJECT DETECTION METHOD ADAPTED T...

引用

2024 International conference on image processing

作者： Pu, Chengdao Yu, Jun Su, Wen Liu, Tianyu Univ Sci & Technol China Hefei 230022 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Zhejiang Sci Tech Univ Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, oriented object detection, as an emerging subfield within object detection, has garnered significant attention. Besides encompassing directional information, datasets of oriented objects exhibit notable characteristics, including significant variations in object scales and a wide range of aspect ratios for ground-truth bounding boxes. Nevertheless, the current state-of-the-art two-stage rotating object detection models have not sufficiently addressed these characteristics, leading to inherent limitations in accuracy. In response to these challenges, we introduce the Rotated RCNN. Our model is the first to introduce trainable anchors in the field of oriented object detection to achieve anchor distributions similar to the ground truth boxes in the oriented object dataset. Furthermore, considering the distinctive traits of oriented ground truth boxes, we have devised a novel strategy for assigning labels to more effectively choose positive and negative samples specifically designed for oriented objects. In the regression phase of the RPN, we introduce shape constraints to alleviate accuracy losses stemming from mismatches between the encoding method and oriented objects. We comprehensively evaluate our model on the DOTAv1.0 and HRSC2016 datasets, demonstrating the effectiveness of our meticulously designed model.

关键词： Object Detection Oriented Object remote sensing image

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2023 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2023

2023 Asia-Pacific Conference on Image Processing, Electronic...

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2023 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2023

The proceedings contain 52 papers. The topics discussed include: improvement of remote sensing image target detection algorithm based on YOLO V5;A Study of Chan-Vese model with the introduction of edge information;real-time monitoring algorithm of muscle state based on sEMG signal;lane detection network with direction context;anomaly pixel detection via dual-branch uncertainty metrics;high precision license plate recognition algorithm in open scene;implementation and design of metro process quality inspection system based on image processing technology;the research on remote sensing image change detection based on deep learning;research on aircraft wheel hub pose detection method based on machine vision;lunar dome detection method based on few-shot object detection;and image enhancement algorithm of foggy sky with sky based on sky segmentation.

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Research and comparison of lightweight U-Net based on GhostNets for medical and remote sensing images 8

Research and comparison of lightweight U-Net based on GhostN...

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8th International conference on Intelligent Computing and signal processing, ICSP 2023

作者： Zheng, Yi He, Qianen Fuzhou University College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9798350302455

U-Net is widely lightened to achieve fast semantic segmentation of medical images and remote sensing images. The cheap operation of the GhostNet series provides new lightening ideas. Currently, the GhostNet series has three versions, GhostNet, G-GhostNet, and GhostNetV2, each of which has its own advantages. In this paper, we realize the lightweight improvement of U-Net with the help of the GhostNet series to provide a reference for the lightweight research of U-Net. First, the backbone parts of GhostNet, G-GhostNet, and GhostNetV2 were used as the encoder embedding model of U-Net in turn, and the convolutional operation of U-Net is replaced by their unitary module. Second, the three models were trained and tested with medical public dataset and remote sensing image dataset to obtain various performance scores of the models. Finally, the scores were compared. The experimental results show that the G-GhostNet-based lightweight model is the most efficient in the remote sensing image segmentation task and achieves real-time segmentation. While in the medical imaging segmentation task, the GhostNet-based lightweight model is relatively faster and the segmentation accuracy is much higher than that of the G-GhostNet-based model, and the model is more efficient. GhostNetV2 is not dominant in both segmentation tasks. © 2023 IEEE.

关键词： Medical imaging

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Accuracy Improvement with Input image Upscaling for In-Orbit Object Detection 28

Accuracy Improvement with Input Image Upscaling for In-Orbit...

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conference on image and signal processing for remote sensing XXVIII

作者： Seo, Daeun Kwon, Jinse Kim, Hyungshin Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781510655386;9781510655379

We are studying in-orbit real-time object detection for remote sensing satellites. Due to the small object size of remote sensing images, it is hard to achieve high detection accuracy, especially for resource-constrained spacecraft computers. Lightweight object detection models such as YOLO and SSD are feasible choices to achieve acceptable detection speed on board. This study proposes an accuracy-improvement method for the lightweight neural networks with an upscaling ratio estimator without retraining the model. The estimator exploits a scaling ratio that determines how much the image should be resized. With our scaling estimator, we have achieved 10.09% higher accuracy than the original YOLOv4-Tiny models with a 40% detection speed overhead.

关键词： In-orbit object detection satellite imagery accuracy improvement lightweight neural network input image size resolution scaling YOLO

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Cloud detection algorithm based on point by point refinement 3

Cloud detection algorithm based on point by point refinement

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3rd International conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Zhang, Jiyin Ocean College Zhejiang University Zhejiang province Zhoushan316021 China

In order to limit the interference of cloud noise on ground scene information, cloud detection has been a hot issue in research on remote sensing image processing. Cloud detection labels the clouds in remote sensing images at the pixel level. The majority of early cloud detection systems rely on manually created feature and threshold segmentation with limited generalizability. remote sensing cloud detection based on deep learning has improved in accuracy and speed thanks to the quick development of convolutional neural networks, but it is still unable to satisfy practical application requirements when dealing with sceneries with variable cloud block size and sparse distribution. To this end, this study proposes a cloud detection algorithm based on point-by-point refinement based on the idea of coarse to fine. Specifically, firstly, the residual module is introduced in the U-Net network to extract more features;secondly, the point-by-point refinement module is designed to filter out the areas in the remote sensing images where the clouds are easily detected wrongly for optimization and re-prediction, and then produce finer-grained and more accurate cloud detection results. The quantitative and qualitative experiments validate the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： remote sensing

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Multiband remote 3D-ViT data fusion 30

Multiband remote 3D-ViT data fusion

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conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Kurz, Aidan G. Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673816;9781510673809

Satellites are equipped with diverse sensors, capable of capturing detailed information across a multitude of wavelengths. The fusion of multispectral data is pivotal to amplify the visual representation of the area of interest. The improvement of information representation allows for enhanced processing, analysis, and other crucial tasks for numerous fields of study, including remote sensing, defense, and material characterization. Previous solutions often utilize traditional signal processing techniques, including principal component analysis (PCA), to accomplish data fusion. By performing fusion on a feature level, extracted information about the area of interest texture and boundaries are combined. The introduction of neural network techniques improved the reconstruction of data similar to the results obtained by conventional inference of humans. For example, the use of deep learning algorithms in conjunction with PCA allowed for refined reduction of redundancy and distortion of spectral data, in comparison to traditional methods alone. The introduction of the Vision Transformer (ViT) architecture, originally developed for two-dimensional image data, has revolutionized image processing tasks, vastly improving performance at the cost of a large quantity of trainable parameters. Recent experimentation has proven that optimizing ViT for efficiency allows for comparable or even superior performance while lessening the computational cost. The transition from 2D to 3D information via utilization of additional depth and spatial data has also led to superior results as the added information allows for better representation of terrain features, making it invaluable for satellite imagery analysis. Combining the principles of ViT and 3D information to process complex satellite data can result in more effective data fusion to achieve a superior level of data visualization of multispectral satellite imagery in an efficient manner.

关键词： Data Fusion Multispectral Fusion remote sensing Deep Learning

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Multi-scale Siamese Dual Decoding Network for remote sensing Tank image Segmentation 23

Multi-scale Siamese Dual Decoding Network for Remote Sensing...

引用

6th International conference on signal processing and Machine Learning, SPML 2023

作者： Liu, Jiashan Zhang, Shanwen Wang, Xuqi Ren, Zhiwen Ding, Zili The School of Electronic Information Xijing University China

ISBN: (纸本)9798400707575

To address the multi-scale problem and interference of differences between data in remote sensing image segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone network for parallel processing and weight sharing. The dilated stratification attention module is designed to extract effective multi-scale features. Different branches can obtain receptive fields of different sizes in order to learn more partition features and suppress background interference. The global attention module is designed to extract common characteristics between datasets and to focus on the characteristics of the datasets, thus extracting connections between datasets. The residual fusion module is also designed for adaptive learning of feature weights of different encoding layers. Experimental results demonstrate that the proposed method has better efficacy and robustness compared to classical networks, and has stronger recognition efficacy for different complexity scenarios, meeting the expectations of this study. © 2023 ACM.

关键词： image segmentation

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Research and application of remote sensing image-based extraction technology for fire trails 3

Research and application of remote sensing image-based extra...

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3rd International conference on signal image processing and Communication, ICSIPC 2023

作者： Huang, Huan Fan, Qiang Wu, Jianrong Liu, Kang Mao, Xianyin Zhang, Yizhao Zhao, Chao Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang550002 China Joint Laboratory of Ice Prevention & Disaster Reducing China Southern Power Grid Co. Ltd. Guiyang550002 China School of Electrical Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9781510670945

This paper proposes a remote sensing image-based method for the extraction of fire trails. Firstly, by acquiring multispectral images of the forest in the study area and pre-processing the multispectral images. A vegetation correlation index is calculated, the vegetation correlation index is normalized, read into the vector range of the study area, and the vector range is cropped. Finally, the cropped area is extracted based on a first threshold, the extracted area is transformed, and the transformed area is filtered based on a second threshold to obtain the final fire-trail area. The invention is able to automate the pre-processing and extraction, using only one vegetation index, and combining with area thresholds, the process is more simplified. © 2023 SPIE.

关键词： Fires

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Multi-feature Based Sea–land Segmentation for Multi-spectral and Panchromatic remote-sensing imagery 7th

Multi-feature Based Sea–land Segmentation for Multi-spectra...

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7th International conference on Computer, Communication and signal processing, ICCCSP 2023

作者： Savitha, D.K. Kethavath, Rajkumar Jamal, Arshad Bangalore093 India

ISBN: (纸本)9783031398100

Robust sea–land segmentation in optical remote-sensing images is difficult task because of complex sea-land environment. Low contrast difference between sea and land in the case of panchromatic images and water color variation in case of multispectral images makes the segmentation process highly challenging. In this paper, we propose a multi-feature sea–land segmentation approach for optical remote sensing images where features such as color information in CMYK, intensity in HSV, statistical information and texture features are used to segment the image into sea and land. Morphological operations are applied to fine tune the segmentation results. In the proposed approach, these features are extracted from resized image and take very less computation time. The proposed approach was tested on Google-Earth images and yields robust, accurate sea–land segmentation results and it works very well on both panchromatic as well as multi-spectral images. © IFIP International Federation for Information processing 2023.

关键词： image segmentation

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