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285 results on '"Roomi S"'

1. NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Author

Liu, Xiaoning, Wu, Zongwei, Li, Ao, Vasluianu, Florin-Alexandru, Zhang, Yulun, Gu, Shuhang, Zhang, Le, Zhu, Ce, Timofte, Radu, Jin, Zhi, Wu, Hongjun, Wang, Chenxi, Ling, Haitao, Cai, Yuanhao, Bian, Hao, Zheng, Yuxin, Lin, Jing, Yuille, Alan, Shao, Ben, Guo, Jin, Liu, Tianli, Wu, Mohao, Feng, Yixu, Hou, Shuo, Lin, Haotian, Zhu, Yu, Wu, Peng, Dong, Wei, Sun, Jinqiu, Zhang, Yanning, Yan, Qingsen, Zou, Wenbin, Yang, Weipeng, Li, Yunxiang, Wei, Qiaomu, Ye, Tian, Chen, Sixiang, Zhang, Zhao, Zhao, Suiyi, Wang, Bo, Luo, Yan, Zuo, Zhichao, Wang, Mingshen, Wang, Junhu, Wei, Yanyan, Sun, Xiaopeng, Gao, Yu, Huang, Jiancheng, Chen, Hongming, Chen, Xiang, Tang, Hui, Chen, Yuanbin, Zhou, Yuanbo, Dai, Xinwei, Qiu, Xintao, Deng, Wei, Gao, Qinquan, Tong, Tong, Li, Mingjia, Hu, Jin, He, Xinyu, Guo, Xiaojie, Sabarinathan, Uma, K, Sasithradevi, A, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Srivatsav, V., Wang, Jinjuan, Sun, Long, Chen, Qiuying, Shao, Jiahong, Zhang, Yizhi, Conde, Marcos V., Feijoo, Daniel, Benito, Juan C., García, Alvaro, Lee, Jaeho, Kim, Seongwan, A, Sharif S M, Khujaev, Nodirkhuja, Tsoy, Roman, Murtaza, Ali, Khairuddin, Uswah, Faudzi, Ahmad 'Athif Mohd, Malagi, Sampada, Joshi, Amogh, Akalwadi, Nikhil, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lian, Wenyi, Lian, Wenjing, Kalyanshetti, Jagadeesh, Aralikatti, Vijayalaxmi Ashok, Yashaswini, Palani, Upasi, Nitish, Hegde, Dikshit, Patil, Ujwala, C, Sujata, Yan, Xingzhuo, Hao, Wei, Fu, Minghan, choksy, Pooja, Sarvaiya, Anjali, Upla, Kishor, Raja, Kiran, Yan, Hailong, Zhang, Yunkai, Li, Baiang, Zhang, Jingyi, and Zheng, Huan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field., Comment: NTIRE 2024 Challenge Report

Published
2024

4. Gender Recognition Using ANN and Forward Rajan Transform Inclusive of Transgender Identity

Author

Priya, K., Roomi, S. Mohamed Mansoor, Maheswari, P. Uma, Farook, Faazelah Mohamed, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chakravarthi, Bharathi Raja, editor, B, Bharathi, editor, García Cumbreras, Miguel Ángel, editor, Jiménez Zafra, Salud María, editor, Subramanian, Malliga, editor, Shanmugavadivel, Kogilavani, editor, and Nakov, Preslav, editor

Published
2024
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9. Shallow CNN Model for Recognition of Infant’s Facial Expression

Author

Maheswari, P. Uma, Roomi, S. Mohamed Mansoor, Senthilarasi, M., Priya, K., Mahadevan, G. Shankar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Singh, Pradeep, editor, Singh, Deepak, editor, Tiwari, Vivek, editor, and Misra, Sanjay, editor

Published
2023
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10. Computer Graphic and Photographic Images Classification—A Transfer Learning Approach

Author

Sasithradevi, A., Shoba, S., Baskar, Chanthini, Mohamed Mansoor Roomi, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar Singh, Koushlendra, editor, Bajpai, Manish Kumar, editor, and Sheikh Akbari, Akbar, editor

Published
2023
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11. Sketch-Based Face Recognition

Author

Maheesha, M., Samiksha, S., Sweety, M., Sathyabama, B., Nagarathna, R., Roomi, S. Mohamed Mansoor, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Tiwari, Ritu, editor, Pavone, Mario F., editor, and Ravindranathan Nair, Ranjith, editor

Published
2023
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13. NTIRE 2020 Challenge on Image Demoireing: Methods and Results

Author

Yuan, Shanxin, Timofte, Radu, Leonardis, Ales, Slabaugh, Gregory, Luo, Xiaotong, Zhang, Jiangtao, Qu, Yanyun, Hong, Ming, Xie, Yuan, Li, Cuihua, Xu, Dejia, Chu, Yihao, Sun, Qingyan, Liu, Shuai, Zong, Ziyao, Nan, Nan, Li, Chenghua, Kim, Sangmin, Nam, Hyungjoon, Kim, Jisu, Jeong, Jechang, Cheon, Manri, Yoon, Sung-Jun, Kang, Byungyeon, Lee, Junwoo, Zheng, Bolun, Liu, Xiaohong, Dai, Linhui, Chen, Jun, Cheng, Xi, Fu, Zhenyong, Yang, Jian, Lee, Chul, Vien, An Gia, Park, Hyunkook, Nathan, Sabari, Beham, M. Parisa, Roomi, S Mohamed Mansoor, Lemarchand, Florian, Pelcat, Maxime, Nogues, Erwan, Puthussery, Densen, S, Hrishikesh P, C V, Jiji, Sinha, Ashish, and Zhao, Xuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

Published
2020

14. Moire Image Restoration using Multi Level Hyper Vision Net

Author

Nathan, D. Sabari, Beham, M. Parisa, and Roomi, S. M. Md Mansoor

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

A moire pattern in the images is resulting from high frequency patterns captured by the image sensor (colour filter array) that appear after demosaicing. These Moire patterns would appear in natural images of scenes with high frequency content. The Moire pattern can also vary intensely due to a minimal change in the camera direction/positioning. Thus the Moire pattern depreciates the quality of photographs. An important issue in demoireing pattern is that the Moireing patterns have dynamic structure with varying colors and forms. These challenges makes the demoireing more difficult than many other image restoration tasks. Inspired by these challenges in demoireing, a multilevel hyper vision net is proposed to remove the Moire pattern to improve the quality of the images. As a key aspect, in this network we involved residual channel attention block that can be used to extract and adaptively fuse hierarchical features from all the layers efficiently. The proposed algorithms has been tested with the NTIRE 2020 challenge dataset and thus achieved 36.85 and 0.98 Peak to Signal Noise Ratio (PSNR) and Structural Similarity (SSIM) Index respectively., Comment: 8 pages , 5 figures

Published
2020

15. Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images

Author

Nathan, D. Sabari, Uma, K., Vinothini, D Synthiya, Bama, B. Sathya, and Roomi, S. M. Md Mansoor

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing
Abstract

Hyperspectral Imaging is the acquisition of spectral and spatial information of a particular scene. Capturing such information from a specialized hyperspectral camera remains costly. Reconstructing such information from the RGB image achieves a better solution in both classification and object recognition tasks. This work proposes a novel light weight network with very less number of parameters about 233,059 parameters based on Residual dense model with attention mechanism to obtain this solution. This network uses Coordination Convolutional Block to get the spatial information. The weights from this block are shared by two independent feature extraction mechanisms, one by dense feature extraction and the other by the multiscale hierarchical feature extraction. Finally, the features from both the feature extraction mechanisms are globally fused to produce the 31 spectral bands. The network is trained with NTIRE 2020 challenge dataset and thus achieved 0.0457 MRAE metric value with less computational complexity., Comment: 6pages,4 figures

Published
2020

17. Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

Author

Sabarinathan, D., Beham, M. Parisa, and Roomi, S. M. Md. Mansoor

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

KiTs19 challenge paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as Hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively., Comment: 9 pages, 3 figures, KiTs19 challenge

Published
2019

18. Fused Feature-Driven ANN Model for Estimating Code-Mixing Level in Audio Samples

Author

Priya, K., Mohamed Mansoor Roomi, S., Alaguraja, R. A., Vasuki, P., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Raj, Jennifer S., editor, Shi, Yong, editor, Pelusi, Danilo, editor, and Balas, Valentina Emilia, editor

Published
2022
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20. Sketch-Based Face Recognition

Author

Maheesha, M., primary, Samiksha, S., additional, Sweety, M., additional, Sathyabama, B., additional, Nagarathna, R., additional, and Roomi, S. Mohamed Mansoor, additional

Published
2022
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22. Recognition of Obscure Objects Using Super Resolution-Based Generative Adversarial Networks

Author

Sathyabama, B., Arunesh, A., SynthiyaVinothini, D., Anupriyadharsini, S., Md. Mansoor Roomi, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Li, Yong, Series Editor, and Gopi, E. S., editor

Published
2021
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23. Novel CNN Architecture for Human Action Recognition

Author

Kalaivani, P., Mohamed Mansoor Roomi, S., Maheesha, M., Subathraa, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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24. Concept Bank Driven Semantic Video Retrieval Model

Author

Sasithradevi, A., Mohamed Mansoor Roomi, S., Bala Kannan, M., Jebas Immanuel, J., Gnana VaruvelRaja, J., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Komanapalli, Venkata Lakshmi Narayana, editor, Sivakumaran, N., editor, and Hampannavar, Santoshkumar, editor

Published
2021
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25. Deep learning based soft computing technique for intelligent attendance management system.

Author

Sriram, K. K., Sivakumar, V., Sathieshkumar, P., Maheswari, P. Uma, and Roomi, S. Mohamed Mansoor

Subjects
SOFT computing, DATA augmentation, ELECTRONIC paper, FEATURE extraction, DEEP learning, ATTENDANCE
Abstract

The use of facial recognition method has spread to many different industries, most notably smart attendance systems. But one major obstacle these systems must overcome is spoofing, which is the practice of people trying to trick the system by utilizing counterfeit or modified photos or videos. In the context of a smart attendance framework, this paper presents a smart anti-spoofing mechanism that can distinguish amongst real and fake faces with accuracy. The system that has been developed entails the generation of datasets that contain both actual and fake facial photos and videos, in addition to the application of a feature extraction and classification pipeline that is driven by deep learning. Furthermore, a number of data augmentation techniques, including translation, scaling, and rotation, are used to improve the effectiveness of the suggested system against different spoofing strategies. Empirical results show that these strategies support increased accuracy and durability in thwarting various spoofing attack types. With a 97% recognition rate overall, our anti-spoofing/mocking technology affords a stable and secure foundation for intelligent attendance systems. The study emphasizes how effective it is to create a strong anti-spoofing system specifically for keen attendance applications by integrating deep learning and soft computing models. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Texture Classification by Local Rajan Transform Based Descriptor

Author

Priya, K., Mohamed Mansoor Roomi, S., Sathyabama, B., Neelavathy, R., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Babu, R. Venkatesh, editor, Prasanna, Mahadeva, editor, and Namboodiri, Vinay P., editor

Published
2020
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43. A Spatio Temporal Texture Saliency Approach for Object Detection in Videos

Author

Sasithradevi, A., Mohamed Mansoor Roomi, S., Sanofer, I., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mukherjee, Snehasis, editor, Mukherjee, Suvadip, editor, Mukherjee, Dipti Prasad, editor, Sivaswamy, Jayanthi, editor, Awate, Suyash, editor, Setlur, Srirangaraj, editor, Namboodiri, Anoop M., editor, and Chaudhury, Santanu, editor

Published
2017
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49. NTIRE 2022 Spectral Recovery Challenge and Data Set

Author

Arad, B, Timofte, R, Yahel, R, Morag, N, Bernat, A, Cai, Y, Lin, J, Lin, Z, Wang, H, Zhang, Y, Pfister, H, Van Gool, L, Liu, S, Li, Y, Feng, C, Lei, L, Li, J, Du, S, Wu, C, Leng, Y, Song, R, Zhang, M, Song, C, Zhao, S, Lang, Z, Wei, W, Zhang, L, Dian, R, Shan, T, Guo, A, Liu, J, Agarla, M, Bianco, S, Buzzelli, M, Celona, L, Schettini, R, He, J, Xiao, Y, Xiao, J, Yuan, Q, Kwon, T, Ryu, D, Bae, H, Yang, H, Chang, H, Huang, Z, Chen, W, Kuo, S, Chen, J, Li, H, Sabarinathan, S, Uma, K, Bama, B, Roomi, S, Arad, Boaz, Timofte, Radu, Yahel, Rony, Morag, Nimrod, Bernat, Amir, Cai, Yuanhao, Lin, Jing, Lin, Zudi, Wang, Haoqian, Zhang, Yulun, Pfister, Hanspeter, Van Gool, Luc, Liu, Shuai, Li, Yongqiang, Feng, Chaoyu, Lei, Lei, Li, Jiaojiao, Du, Songcheng, Wu, Chaoxiong, Leng, Yihong, Song, Rui, Zhang, Mingwei, Song, Chongxing, Zhao, Shuyi, Lang, Zhiqiang, Wei, Wei, Zhang, Lei, Dian, Renwei, Shan, Tianci, Guo, Anjing, Feng, Chengguo, Liu, Jinyang, Agarla, Mirko, Bianco, Simone, Buzzelli, Marco, Celona, Luigi, Schettini, Raimondo, He, Jiang, Xiao, Yi, Xiao, Jiajun, Yuan, Qiangqiang, Li, Jie, Zhang, Liangpei, Kwon, Taesung, Ryu, Dohoon, Bae, Hyokyoung, Yang, Hao-Hsiang, Chang, Hua-En, Huang, Zhi-Kai, Chen, Wei-Ting, Kuo, Sy-Yen, Chen, Junyu, Li, Haiwei, Liu, Song, Sabarinathan, Sabarinathan, Bama, B Sathya, Roomi, S. Mohamed Mansoor, Arad, B, Timofte, R, Yahel, R, Morag, N, Bernat, A, Cai, Y, Lin, J, Lin, Z, Wang, H, Zhang, Y, Pfister, H, Van Gool, L, Liu, S, Li, Y, Feng, C, Lei, L, Li, J, Du, S, Wu, C, Leng, Y, Song, R, Zhang, M, Song, C, Zhao, S, Lang, Z, Wei, W, Zhang, L, Dian, R, Shan, T, Guo, A, Liu, J, Agarla, M, Bianco, S, Buzzelli, M, Celona, L, Schettini, R, He, J, Xiao, Y, Xiao, J, Yuan, Q, Kwon, T, Ryu, D, Bae, H, Yang, H, Chang, H, Huang, Z, Chen, W, Kuo, S, Chen, J, Li, H, Sabarinathan, S, Uma, K, Bama, B, Roomi, S, Arad, Boaz, Timofte, Radu, Yahel, Rony, Morag, Nimrod, Bernat, Amir, Cai, Yuanhao, Lin, Jing, Lin, Zudi, Wang, Haoqian, Zhang, Yulun, Pfister, Hanspeter, Van Gool, Luc, Liu, Shuai, Li, Yongqiang, Feng, Chaoyu, Lei, Lei, Li, Jiaojiao, Du, Songcheng, Wu, Chaoxiong, Leng, Yihong, Song, Rui, Zhang, Mingwei, Song, Chongxing, Zhao, Shuyi, Lang, Zhiqiang, Wei, Wei, Zhang, Lei, Dian, Renwei, Shan, Tianci, Guo, Anjing, Feng, Chengguo, Liu, Jinyang, Agarla, Mirko, Bianco, Simone, Buzzelli, Marco, Celona, Luigi, Schettini, Raimondo, He, Jiang, Xiao, Yi, Xiao, Jiajun, Yuan, Qiangqiang, Li, Jie, Zhang, Liangpei, Kwon, Taesung, Ryu, Dohoon, Bae, Hyokyoung, Yang, Hao-Hsiang, Chang, Hua-En, Huang, Zhi-Kai, Chen, Wei-Ting, Kuo, Sy-Yen, Chen, Junyu, Li, Haiwei, Liu, Song, Sabarinathan, Sabarinathan, Bama, B Sathya, and Roomi, S. Mohamed Mansoor

Abstract

This paper reviews the third biennial challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image. This challenge presents the "ARAD_1K" data set: a new, larger-than-ever natural hyperspectral image data set containing 1,000 images. Challenge participants were required to recover hyper-spectral information from synthetically generated JPEG-compressed RGB images simulating capture by a known calibrated camera, operating under partially known parameters, in a setting which includes acquisition noise. The challenge was attended by 241 teams, with 60 teams com-peting in the final testing phase, 12 of which provided de-tailed descriptions of their methodology which are included in this report. The performance of these submissions is re-viewed and provided here as a gauge for the current state-of-the-art in spectral reconstruction from natural RGB images.

Published
2022

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