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2. 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

17. NTIRE 2022 Spectral Recovery Challenge and Data Set

Author

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

Subjects
spectral reconstruction, spectral recovery
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

19. Global COVID-19 lockdown highlights humans as both threats and custodians of the environment.

Author

Bates AE, Primack RB, Biggar BS, Bird TJ, Clinton ME, Command RJ, Richards C, Shellard M, Geraldi NR, Vergara V, Acevedo-Charry O, Colón-Piñeiro Z, Ocampo D, Ocampo-Peñuela N, Sánchez-Clavijo LM, Adamescu CM, Cheval S, Racoviceanu T, Adams MD, Kalisa E, Kuuire VZ, Aditya V, Anderwald P, Wiesmann S, Wipf S, Badihi G, Henderson MG, Loetscher H, Baerenfaller K, Benedetti-Cecchi L, Bulleri F, Bertocci I, Maggi E, Rindi L, Ravaglioli C, Boerder K, Bonnel J, Mathias D, Archambault P, Chauvaud L, Braun CD, Thorrold SR, Brownscombe JW, Midwood JD, Boston CM, Brooks JL, Cooke SJ, China V, Roll U, Belmaker J, Zvuloni A, Coll M, Ortega M, Connors B, Lacko L, Jayathilake DRM, Costello MJ, Crimmins TM, Barnett L, Denny EG, Gerst KL, Marsh RL, Posthumus EE, Rodriguez R, Rosemartin A, Schaffer SN, Switzer JR, Wong K, Cunningham SJ, Sumasgutner P, Amar A, Thomson RL, Stofberg M, Hofmeyr S, Suri J, Stuart-Smith RD, Day PB, Edgar GJ, Cooper AT, De Leo FC, Garner G, Des Brisay PG, Schrimpf MB, Koper N, Diamond MS, Dwyer RG, Baker CJ, Franklin CE, Efrat R, Berger-Tal O, Hatzofe O, Eguíluz VM, Rodríguez JP, Fernández-Gracia J, Elustondo D, Calatayud V, English PA, Archer SK, Dudas SE, Haggarty DR, Gallagher AJ, Shea BD, Shipley ON, Gilby BL, Ballantyne J, Olds AD, Henderson CJ, Schlacher TA, Halliday WD, Brown NAW, Woods MB, Balshine S, Juanes F, Rider MJ, Albano PS, Hammerschlag N, Hays GC, Esteban N, Pan Y, He G, Tanaka T, Hensel MJS, Orth RJ, Patrick CJ, Hentati-Sundberg J, Olsson O, Hessing-Lewis ML, Higgs ND, Hindell MA, McMahon CR, Harcourt R, Guinet C, Hirsch SE, Perrault JR, Hoover SR, Reilly JD, Hobaiter C, Gruber T, Huveneers C, Udyawer V, Clarke TM, Kroesen LP, Hik DS, Cherry SG, Del Bel Belluz JA, Jackson JM, Lai S, Lamb CT, LeClair GD, Parmelee JR, Chatfield MWH, Frederick CA, Lee S, Park H, Choi J, LeTourneux F, Grandmont T, de-Broin FD, Bêty J, Gauthier G, Legagneux P, Lewis JS, Haight J, Liu Z, Lyon JP, Hale R, D'Silva D, MacGregor-Fors I, Arbeláez-Cortés E, Estela FA, Sánchez-Sarria CE, García-Arroyo M, Aguirre-Samboní GK, Franco Morales JC, Malamud S, Gavriel T, Buba Y, Salingré S, Lazarus M, Yahel R, Ari YB, Miller E, Sade R, Lavian G, Birman Z, Gury M, Baz H, Baskin I, Penn A, Dolev A, Licht O, Karkom T, Davidzon S, Berkovitch A, Yaakov O, Manenti R, Mori E, Ficetola GF, Lunghi E, March D, Godley BJ, Martin C, Mihaly SF, Barclay DR, Thomson DJM, Dewey R, Bedard J, Miller A, Dearden A, Chapman J, Dares L, Borden L, Gibbs D, Schultz J, Sergeenko N, Francis F, Weltman A, Moity N, Ramírez-González J, Mucientes G, Alonso-Fernández A, Namir I, Bar-Massada A, Chen R, Yedvab S, Okey TA, Oppel S, Arkumarev V, Bakari S, Dobrev V, Saravia-Mullin V, Bounas A, Dobrev D, Kret E, Mengistu S, Pourchier C, Ruffo A, Tesfaye M, Wondafrash M, Nikolov SC, Palmer C, Sileci L, Rex PT, Lowe CG, Peters F, Pine MK, Radford CA, Wilson L, McWhinnie L, Scuderi A, Jeffs AG, Prudic KL, Larrivée M, McFarland KP, Solis R, Hutchinson RA, Queiroz N, Furtado MA, Sims DW, Southall E, Quesada-Rodriguez CA, Diaz-Orozco JP, Rodgers KS, Severino SJL, Graham AT, Stefanak MP, Madin EMP, Ryan PG, Maclean K, Weideman EA, Şekercioğlu ÇH, Kittelberger KD, Kusak J, Seminoff JA, Hanna ME, Shimada T, Meekan MG, Smith MKS, Mokhatla MM, Soh MCK, Pang RYT, Ng BXK, Lee BPY, Loo AHB, Er KBH, Souza GBG, Stallings CD, Curtis JS, Faletti ME, Peake JA, Schram MJ, Wall KR, Terry C, Rothendler M, Zipf L, Ulloa JS, Hernández-Palma A, Gómez-Valencia B, Cruz-Rodríguez C, Herrera-Varón Y, Roa M, Rodríguez-Buriticá S, Ochoa-Quintero JM, Vardi R, Vázquez V, Requena-Mesa C, Warrington MH, Taylor ME, Woodall LC, Stefanoudis PV, Zhang X, Yang Q, Zukerman Y, Sigal Z, Ayali A, Clua EEG, Carzon P, Seguine C, Corradini A, Pedrotti L, Foley CM, Gagnon CA, Panipakoochoo E, Milanes CB, Botero CM, Velázquez YR, Milchakova NA, Morley SA, Martin SM, Nanni V, Otero T, Wakeling J, Abarro S, Piou C, Sobral AFL, Soto EH, Weigel EG, Bernal-Ibáñez A, Gestoso I, Cacabelos E, Cagnacci F, Devassy RP, Loretto MC, Moraga P, Rutz C, and Duarte CM

Abstract

The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness., Competing Interests: Authors declare no competing interests., (© 2021 Published by Elsevier Ltd.)

Published
2021
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20. Turbulence effects on high energy laser beam propagation in the atmosphere.

Author

Yahel RZ

Abstract

A simple phenomenological model for the interaction of a high energy laser (HEL) beam with atmospheric turbulence is presented. According to this model the temperature power spectrum becomes non-Kolmogorov. The refractive index structure constant C(2)(n) is replaced by another quantity C(2)(n,eff) which is space dependent, C(2)(n,eff) = C(2)(n) (1 + mid R:nablaT/ partial differentialtheta / partial differentialzmid R:(2)) (provided ( partial differentialtheta )/( partial differentialz) not equal 0). Here DeltaT is the local temperature gradient in the crosswind direction, while ( partial differentialtheta )/( partial differentialz) is the vertical potential temperature gradient in the atmosphere. Numerical simulations of HEL beam propagation were performed including effects of this model. Implications of the results are discussed.

Published
1990
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