IEEE Geoscience and Remote Sensing Magazine - September 2022 - 248

Remote Sens., vol. 9, no. 6, p. 586, Jun. 2017, doi: 10.3390/
rs9060586.
[36] L. Wald, T. Ranchin, and M. Mangolini, " Fusion of satellite
images of different spatial resolutions: Assessing the quality
of resulting images, " Photogram. Eng. Remote Sens., vol. 63, no.
6, pp. 691-699, 1997. [Online]. Available: https://hal.archives
-ouvertes.fr/hal-00365304
[37] Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, " Image quality
assessment: from error visibility to structural similarity, "
IEEE Trans. Image Process., vol. 13, no. 4, pp. 600-612, Apr.
2004, doi: 10.1109/TIP.2003.819861.
[38] C. R. Helmrich, S. Bosse, M. Siekmann, H. Schwarz, D. Marpe,
and T. Wiegand, " Perceptually optimized bit-allocation and
associated distortion measure for block-based image or video
coding, " in Proc. Data Compress. Conf. (DCC), Snowbird, UT,
USA, Mar. 2019, pp. 172-181, doi: 10.1109/DCC.2019.00025.
[39] Z. Wang and A. Bovik, " A universal image quality index, " IEEE
Signal Process. Lett., vol. 9, no. 3, pp. 81-84, Mar. 2002, doi:
10.1109/97.995823.
[40] Z. Wang, E. Simoncelli, and A. Bovik, " Multiscale structural
similarity for image quality assessment, " in Proc. 37th Asilomar
Conf. Signals, Syst. Comput., 2003, pp. 1398-1402. [Online].
Available: http://ieeexplore.ieee.org/document/1292216/
[41] H. Sheikh, A. Bovik, and G. de Veciana, " An information fidelity
criterion for image quality assessment using natural scene
statistics, " IEEE Trans. Image Process., vol. 14, no. 12, pp. 2117-
2128, Dec. 2005, doi: 10.1109/TIP.2005.859389.
[42] H. Sheikh and A. Bovik, " Image information and visual quality, "
IEEE Trans. Image Process., vol. 15, no. 2, pp. 430-444, Feb.
2006, doi: 10.1109/TIP.2005.859378.
[43] N. Damera-Venkata, T. Kite, W. Geisler, B. Evans, and A. Bovik,
" Image quality assessment based on a degradation model, "
IEEE Trans. Image Process., vol. 9, no. 4, pp. 636-650, Apr. 2000,
doi: 10.1109/83.841940.
[44] L. Zhang, L. Zhang, X. Mou, and D. Zhang, " FSIM: A feature
similarity index for image quality assessment, " IEEE Trans.
Image Process., vol. 20, no. 8, pp. 2378-2386, Aug. 2011, doi:
10.1109/TIP.2011.2109730.
[45] A. Liu, W. Lin, and M. Narwaria, " Image quality assessment
based on gradient similarity, " IEEE Trans. Image Process.,
vol. 21, no. 4, pp. 1500-1512, Apr. 2012, doi: 10.1109/
TIP.2011.2175935.
[46] R. H. Yuhas, A. F. Goetz, and J. W. Boardman, " Discrimination
among semi-arid landscape endmembers using the spectral
angle mapper (SAM) algorithm, " in Proc. 3rd Annu. JPL Airborne
Earth Sci. Workshop, Pasadena, CA, USA, Jun. 1992. [Online].
Available: https://aviris.jpl.nasa.gov/proceedings/work
shops/92\_ docs/52.PDF
[47] L. Wald, " Quality of high resolution synthesised images: Is
there a simple criterion? " in Proc. 3rd Conf. Fusion Earth Data:
Merging Point Meas., Raster Maps Remotely Sensed Images, Jan.
2000, pp. 99-103.
[48] D. M. Chandler, " Most apparent distortion: Full-reference
image quality assessment and the role of strategy, "
J. Electron. Imaging, vol. 19, no. 1, p. 11,006, Jan. 2010, doi:
10.1117/1.3267105.
248
[49] J. Johnson, A. Alahi, and L. Fei-Fei, " Perceptual losses for real-time
style transfer and super-resolution, " Mar. 2016, arXiv:
1603.08155.
[50] A. Mittal, A. K. Moorthy, and A. C. Bovik, " No-reference image
quality assessment in the spatial domain, " IEEE Trans.
Image Process., vol. 21, no. 12, pp. 4695-4708, Dec. 2012, doi:
10.1109/TIP.2012.2214050.
[51] A. Mittal, R. Soundararajan, and A. C. Bovik, " Making a completely
blind image quality analyzer, " IEEE Signal Process.
Lett., vol. 20, no. 3, pp. 209-212, Mar. 2013, doi: 10.1109/
LSP.2012.2227726.
[52] N. Venkatanath, D. Praneeth, B. Maruthi Chandrasekhar, S.
S. Channappayya, and S. S. Medasani, " Blind image quality
evaluation using perception-based features, " in Proc. 21st
Nat. Conf. Commun. (NCC), Feb. 2015, pp. 1-6, doi: 10.1109/
NCC.2015.7084843.
[53] C. Ma, C.-Y. Yang, X. Yang, and M.-H. Yang, " Learning a no-reference
quality metric for single-image super-resolution, " Dec.
2016, arXiv:1612.05890.
[54] Y. Blau, R. Mechrez, R. Timofte, T. Michaeli, and L. ZelnikManor,
" The 2018 PIRM challenge on perceptual image superresolution, "
Jan. 2019, arXiv: 1809.07517.
[55] R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang,
" The unreasonable effectiveness of deep features as a perceptual
metric, " Apr. 2018, arXiv: 1801.03924.
[56] L. Alparone, B. Aiazzi, S. Baronti, A. Garzelli, F. Nencini,
and M. Selva, " Multispectral and panchromatic data fusion
assessment without reference, " Photogram. Eng. Remote
Sens., vol. 74, no. 2, pp. 193-200, Feb. 2008, doi: 10.14358/
PERS.74.2.193.
[57] Y. Blau and T. Michaeli, " The perception-distortion tradeoff, "
in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018,
arXiv: 1711.06077.
[58] I. J. Goodfellow et al., " Generative adversarial nets, " in Proc.
27th Int. Conf. Neural Inf. Process. Syst. - vol. 2 (NIPS' 14). Cambridge,
MA, USA: MIT Press, 2014, pp. 2672-2680.
[59] M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, " Deconvolutional
networks, " in Proc. IEEE Comput. Soc. Conf. Comput.
Vis. Pattern Recognit., Jun. 2010, pp. 2528-2535.
[60] A. Odena, V. Dumoulin, and C. Olah, " Deconvolution and
checkerboard artifacts, " Distill, vol. 1, no. 10, 2016, doi:
10.23915/distill.00003.
[61] W. Shi et al., " Real-time single image and video super-resolution
using an efficient sub-pixel convolutional neural network, "
Sep. 2016, arXiv: 1609.05158.
[62] W. Shi et al., " Is the deconvolution layer the same as a convolutional
layer? " Sep. 2016, arXiv: 1609.07009.
[63] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, " Beyond
a Gaussian denoiser: Residual learning of deep CNN for image
denoising, " IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142-
3155, Jul. 2017, doi: 10.1109/TIP.2017.2662206.
[64] K. He, X. Zhang, S. Ren, and J. Sun, " Deep residual learning for
image recognition, " Dec. 2015, arXiv: 1512.03385.
[65] P. Burt and E. Adelson, " The Laplacian pyramid as a compact
image code, " IEEE Trans. Commun., vol. 31, no. 4, pp. 532-540,
Apr. 1983, doi: 10.1109/TCOM.1983.1095851.
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2022
https://hal.archives-ouvertes.fr/hal-00365304 https://hal.archives-ouvertes.fr/hal-00365304 http://ieeexplore.ieee.org/document/1292216/ https://aviris.jpl.nasa.gov/proceedings/workshops/92\_docs/52.PDF https://aviris.jpl.nasa.gov/proceedings/workshops/92\_docs/52.PDF
IEEE Geoscience and Remote Sensing Magazine - September 2022

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2022
