# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 3 Results
### Testing Single-Image, Super-Resolution Configurations and Designing a Multi-Scale Adaptive Model
Not too surprisingly, in the first case, higher RMSD are found along some of the repeated altimeter tracks, well visible as diamond/rhomboid shapes (Figure 2B), which again indicates that the network is not able to efficiently exploit the information on the  
Figure 2: Enhanced Deep Super-Resolution (EDSR) baseline model tested for the reconstruction of high-resolution absolute dynamic topography from multiple channel inputs. (**A**) EDSR network architecture. EDSR is based on a specific residual block design. (**B,C**) Relative performance of the EDSR reconstructions, assessed on the independent test data as the difference between the RMSD between the altimeter-like and original model output and the RMSD between the super-resolved ADT and the original model ADT (red indicates smaller RMSD from model predictions). The panels show the performance of the models trained/tested considering: (**B**) EDSR and the full set of predictor variables; (**C**) EDSR removing \(\Delta\)ADT from the predictors. ADT and related RMSD values are expressed in m.  
ADT interpolation error. In the latter, though, higher errors in the reconstruction are found also in some areas that are well known for being dynamically very active (e.g., offshore the Algerian coast, Figure 2C). The third network considered is the Adaptive Deep Residual Network for Super-Resolution (ADR-SR) proposed by [58]. ADR-SR represents an interesting evolution of the EDSR. Its main improvement consists in substituting the learned feature fixed scaling applied within the EDSR residual block with an adaptive scaling, obtained by introducing a squeeze-and-excitation (SE) module [59]. Within the adaptive residual block (ARB), channel-wise feature responses are adaptively recalibrated through an SE module before summing them to the block input, allowing the network to more efficiently model complex interdependencies between the learned feature channels (Figure 3A). Conceptually, we might thus expect it to drive a substantial advance with respect to simpler networks. The SE module first reduces all 2D feature channels into 1D values through global average pooling (squeeze). The excitation operation then consists in learning a weight vector, built through a self-gating mechanism that takes the output of the global average pooling as input and provides per-channel modulation weights. The self-gating consists in a bottleneck with two, fully connected layers (including non-linear activations), the first  
Figure 3: Adaptive Super-Resolution (ADR-SR) baseline model tested for the reconstruction of high-resolution absolute dynamic topography from multiple channel inputs. (**A**) ADR-SR network. ADR-SR is based on the inclusion of a squeeze-and-excitation module within its residual block design. (**B,C**) Relative performance of the ADR-SR reconstructions, assessed on the independent test data as the difference between the RMSD between the altimeter-like and original model output and the RMSD between the super-resolved ADT and the original model ADT (red indicates smaller RMSD from model predictions). The panels show the performance of the models trained/tested considering: (**B**) ADR-SR and the full set of predictor variables; (**C**) ADR-SR removing AADT from the predictors. ADT and related RMSD values are expressed in m.  
one reducing the dimensionality by a predefined factor (and including a ReLU activation), and the second one increasing it back to the number of channels in the input to the module (followed by a sigmoid activation). These weights are successively used to suppress or enhance individual channel features (feature recalibration) before summation to get the residuals. With respect to baseline EDSR, ADR-SR increases the number of filters within each block from 64 to 192 but limits the number of feature channels in input to the residual block from 64 to 32. The ADR-SR network implemented here employs 16 residual blocks and finally includes slightly less than 1.8M trainable parameters.