# Task-Driven Real-World Super-Resolution of Document Scans
## 4 Experimental Validation
### Experimental Setup
#### 4.1.2 Training Strategy  
The SRResNet model was initialized using weights from a variant pretrained on the MS COCO dataset with the pixel-wise MSE loss, following the procedure described in [9]. To investigate the effect of degradation realism on SR performance, we fine-tuned the model, relying on both real-world and simulated datasets. In the former case, we  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No. of** & **LR–HR Resolution** & **LR Patch Size** & \multicolumn{2}{c}{**No. of Patches**} \\ \cline{3-6}
**Data Source** & **Images** & **(in DPI)** & **(in px)** & **Real-World** & **Simulated** \\ \hline
**Pretraining set** & & & & & \\ MS COCO [28] & 81,574 & — & 32 \(\times\) 32 & — & 81,574 \\ \hline
**Training and validation set** & & & & & \\ University Bulletin & 208 & 75–300 & 256 \(\times\) 256 & 2046 & 2046 \\  & 208 & 150–600 & 256 \(\times\) 256 & 6004 & 6004 \\  & 48 & 75–300 & 256 \(\times\) 256 & 482 & 482 \\  & 48 & 150–600 & 256 \(\times\) 256 & 1392 & 1392 \\ \hline
**Test set** & & & & & \\ MS COCO [28] & 1209 & — & 32 \(\times\) 32 & — & 1209 \\ University Bulletin & 26 & 75–300 & 256 \(\times\) 256 & 300 & 300 \\  & 26 & 150–600 & 256 \(\times\) 256 & 875 & 875 \\ Scientific Article & 6 & 75–300 & 256 \(\times\) 256 & 72 & 72 \\  & 6 & 150–600 & 256 \(\times\) 256 & 210 & 210 \\ COVID Test Leaflet & 1 & 75–300 & 128 \(\times\) 128 & 95 & 95 \\ Old Books [45] & 45 & 125–500 & 256 \(\times\) 256 & 860 & 860 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed specification of all datasets used in our study. For real LR–HR pairs, LR scans were acquired at specific DPI settings and aligned to HR references. Simulated counterparts were generated by the bicubic downsampling of HR images to match the number and size of real-world LR patches.  
trained the model using image pairs acquired through actual scanning processes from the University Bulletin and Scientific Article datasets, specifically at resolution ratios of 4\(\times\) (e.g., 75-300 DPI and 150-600 DPI). These pairs contain realistic distortions, including scanner-specific blur, noise, compression artifacts, and physical imperfections from printed media. This setup enables the model to learn reconstruction patterns reflective of real-world scanning degradations. Furthermore, we exploited simulated datasets--we used the same HR images as in the case of real-world datasets, but the LR counterparts were obtained by bicubic downsampling. This allowed for controlled degradation modeling under matched semantic content, isolating the learning dynamics attributable solely to the downsampling process. This protocol was designed to disentangle the contributions of physical acquisition artifacts and interpolation-based degradation, facilitating a fair comparison between models trained under real-world and simulated conditions.  
To balance the multiple objectives of our task-driven framework, we employed the DWA strategy for adaptive loss weighting. In practice, we observed that DWA allowed the model to dynamically shift focus depending on input characteristics: for instance, when processing patches with sparse or low-contrast text, the contribution of CRNN-based supervision increased, whereas for the texture-rich regions a greater emphasis was placed on pixel-wise losses and Key.Net-based structural alignment. This adaptive mechanism promoted stable optimization and contributed to the model's ability to generalize effectively across the test dataset.