## 2 Related Work
### Deep Learning-Based Image Rectification  
In recent years, deep neural networks have demonstrated remarkable advantages in image distortion correction tasks due to their powerful capacity to model complex nonlinear transformations [22]. Compared with traditional geometric models, deep learning approaches can automatically learn the mapping between distorted and corrected images from data, offering greater flexibility, especially in handling non-parametric and multi-scale deformations [3]. Existing studies have primarily explored three methodological pathways: spatial transformation techniques, control point/deformation field prediction, and hybrid strategies incorporating optical flow estimation [27; 28; 29].  
Early methods were represented by the Spatial Transformer Network (STN) [27], which performs geometric alignment via affine parameter prediction. ST-GAN further improved structural fidelity through adversarial training [30], while GridNet introduced asparse grid-based control mechanism to enhance local adaptability [31]. Although these approaches feature compact architectures and stable optimization, they often suffer from under-correction when faced with severe edge distortions in wide-angle imagery.  
To enhance local modeling capability, control point-based methods using TPS interpolation have been proposed. For example, TPS Transform Net predicts sparse control point locations via deep networks to reconstruct geometric consistency in distorted images [32; 33], showing improved correction performance, particularly in peripheral regions. Building on this, methods such as DenseRegWarp estimate pixel-wise deformation fields for fine-grained alignment [34]; however, their complex architectures and computational demands hinder real-time inference and limit deployment on edge devices.  
In recent developments, optical flow estimation has been widely integrated into distortion correction tasks as a structure-preserving constraint. Models like RAFT predict dense flow fields through a recurrent architecture, achieving outstanding performance in local feature alignment and structural continuity [29]. Studies have shown that incorporating optical flow as a prior effectively mitigates feature mismatch and enhances edge fidelity [35]. Nonetheless, their high computational cost remains a critical bottleneck for deployment in embedded systems [36].  
In summary, deep learning has significantly advanced the field of image distortion correction--from global alignment via STN to non-rigid modeling with TPS-based networks and structure-aware optimization through optical flow constraints. These methods have demonstrated strong performance on benchmark datasets. However, challenges such as large model size, high inference latency, and deployment inefficiencies still impede their suitability for real-time edge applications. To address this gap, this study proposes an efficient correction framework that integrates lightweight architectures, optical flow-based structural constraints, and multi-scale feature extraction. The goal is to enable low-latency, high-precision correction of complex distortions, thereby enhancing the feasibility of real-world engineering deployment.