# Task-Driven Real-World Super-Resolution of Document Scans
## 2 Related Work
### Deep Learning for SR  
The introduction of CNNs has revolutionized the field of SISR. The SRCNN model by [PERSON] et al., composed of just three convolutional layers, outperformed the sparse-coding techniques in terms of the PSNR score reported for natural images [18]. However, this was achieved at the cost of over-smoothed edges and poor generalization beyond bicubic downsampling. It was followed by the very deep SR (VDSR) network composed of 20 layers, which improved reconstruction quality via residual learning [8]. However, it was originally trained on simulated training pairs, which limits its robustness against real-world degradations. [PERSON] et al. introduced SRResNet, a generator built from deep residual blocks, achieving state-of-the-art scores for Set5 and Set14 datasets [9]. SRResNet also formed the backbone of SRGAN [9], which augments the L2 loss function with an adversarial perceptual loss based on the features extracted using the VGG network [19]. This combination fixed the blurry edges and low realism seen in earlier models, which boosted the perceptual scores, but it also deteriorated the PSNR metric and introduced some artifacts. Subsequent architectures, such as enhanced deep SR network (EDSR) [20]  
Figure 1: Outline of the proposed task-driven training of an SR network. CNNs performing OCR-related tasks are applied to process both the HR reference and the super-resolved image—the differences between their outcomes establish task-driven components of the loss function, which are coupled with the commonly applied image-driven components to guide the training of the SR network.  
and a residual dense network (RDN) [21], refined residual and attention mechanisms to push perceptual quality further. However, these models are more likely to hallucinate, generating plausible results, which do not necessarily reflect the actual ground truth [22]. This limits their applicability in the context of specific tasks like the OCR case considered here. Furthermore, vision transformers have also been successfully employed for SISR [23]. However, these methods focus primarily on natural-image benchmarks with synthetic downsampling and do not integrate higher-level semantic objectives during training.  
Recent developments in SR architectures increasingly emphasize reducing model size without compromising reconstruction quality [24]. [PERSON] et al. have shown that, with vision transformers, it is possible to dynamically adjust feature map sizes to lower model complexity [25]. With just \(6.5\cdot 10^{5}\) parameters, this approach has been reported to outperform many state-of-the-art models that are significantly larger. However, it is sensitive to hyperparameter tuning and still shows higher latency than tiny CNNs on low-resolution inputs. The swift parameter-free attention network [26] introduces a novel attention mechanism devoid of parameters, striking a balance between image quality and inference speed, making it suitable for real-time applications. [PERSON] et al. employed kernel distillation to simplify the model structure and enhance the attention modules [27], reducing the computational cost while improving the performance.  
In sum, even the best generic SR models now deliver high fidelity with tiny parameter budgets, yet they still learn from synthetic degradations and optimize only pixel-wise error. It is therefore not ensured that they preserve task-relevant details--an issue the next section on task-specific SR tackles directly.