Abstract
Single-image super-resolution refers to the reconstruction of a high-resolution image from a single low-resolution observation. Although recent deep learning-based methods have demonstrated notable success on simulated datasets -- with low-resolution images obtained by degrading and downsampling high-resolution ones -- they frequently fail to generalize to real-world settings, such as document scans, which are affected by complex degradations and semantic variability. In this study, we introduce a task-driven, multi-task learning framework for training a super-resolution network specifically optimized for optical character recognition tasks. We propose to incorporate auxiliary loss functions derived from high-level vision tasks, including text detection using the connectionist text proposal network, text recognition via a convolutional recurrent neural network, keypoints localization using this http URL, and hue consistency. To balance these diverse objectives, we employ dynamic weight averaging mechanism, which adaptively adjusts the relative importance of each loss term based on its convergence behavior. We validate our approach upon the SRResNet architecture, which is a well-established technique for single-image super-resolution. Experimental evaluations on both simulated and real-world scanned document datasets demonstrate that the proposed approach improves text detection, measured with intersection over union, while preserving overall image fidelity. These findings underscore the value of multi-objective optimization in super-resolution models for bridging the gap between simulated training regimes and practical deployment in real-world scenarios.
Abstract (translated)
单图像超分辨率指的是从单一低分辨率观测中重建高分辨率图像的过程。尽管基于深度学习的方法在模拟数据集上取得了显著的成功——通过降级和下采样高质量的原始图像来获取低质量图像，但在实际场景如文档扫描中的应用效果却大打折扣。这些实际场景受到复杂的退化因素和语义变化的影响。为此，在这项研究中，我们介绍了一种任务驱动的多任务学习框架，用于训练专为光学字符识别（OCR）任务优化的超分辨率网络。我们的方法通过加入由高层次视觉任务导出的辅助损失函数来增强模型性能，包括使用连接主义文本提议网络进行文本检测、利用卷积递归神经网络实现文本识别、基于this http URL的方法进行关键点定位以及色相一致性。 为了平衡这些不同的目标，我们采用了一种动态权重平均机制，该机制根据每个损失项的收敛行为自适应地调整其相对重要性。我们在SRResNet架构上验证了这种方法的效果，这是一种广泛应用于单图像超分辨率处理的技术。实验结果显示，在模拟和真实世界的文档扫描数据集上的评估中，所提出的方案在提高文本检测准确率（用交并比衡量）的同时保持了整体图像的保真度。这些结果强调了多目标优化在超分辨率模型中的价值，特别是在将仿真训练与实际应用场景相结合时的作用。
URL
https://arxiv.org/abs/2506.06953
