TY  - CONF
AU  - Mohamed Ramzy Ibrahim
AU  - Robert Benavente
AU  - Felipe Lumbreras
AU  - Daniel Ponsa
A2  - CVPRW
PY  - 2022//
TI  - 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks
BT  - CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition)
KW  - Training
KW  - Solid modeling
KW  - Three-dimensional displays
KW  - PSNR
KW  - Convolution
KW  - Superresolution
KW  - Pattern recognition
N2  - The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.
L1  - http://158.109.8.37/files/IBL2022.pdf
UR  - http://dx.doi.org/10.1109/CVPRW56347.2022.00047
N1  - MSIAU; 600.130
ID  - Mohamed Ramzy Ibrahim2022
ER  -